Journal articles on the topic 'Landslide assessment'
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1
Litoseliti, Aspasia, Ioannis K. Koukouvelas, Konstantinos G. Nikolakopoulos, and Vasiliki Zygouri. "An Event-Based Inventory Approach in Landslide Hazard Assessment: The Case of the Skolis Mountain, Northwest Peloponnese, Greece." ISPRS International Journal of Geo-Information 9, no. 7 (July 20, 2020): 457. http://dx.doi.org/10.3390/ijgi9070457.
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Assessment of landslide hazard across mountains is imperative for public safety. Pre- and post-earthquake landslide mapping envisage that landslides show significant size changes during earthquake activity. One of the purposes of earthquake-induced landslide investigation is to determine the landslide state and geometry and draw conclusions on their mobility. This study was based on remote sensing data that covered 72 years, and focused on the west slopes of the Skolis Mountains, in the northwest Peloponnese. On 8 June 2008, during the strong Movri Mountain earthquake (Mw = 6.4), we mapped the extremely abundant landslide occurrence. Historical seismicity and remote sensing data indicate that the Skolis Mountain west slope is repeatedly affected by landslides. The impact of the earthquakes was based on the estimation of Arias intensity in the study area. We recognized that 89 landslides developed over the last 72 years. These landslides increased their width (W), called herein as inflation or their length (L), termed as enlargement. Length and width changes were used to describe their aspect ratio (L/W). Based on the aspect ratio, the 89 landslides were classified into three types: I, J, and Δ. Taluses, developed at the base of the slope and belonging to the J- and Δ-landslide types, are supplied by narrow or irregular channels. During the earthquakes, the landslide channels migrated upward and downward, outlining the mobility of the earthquake-induced landslides. Landslide mobility was defined by the reach angle. The reach angle is the arctangent of the landslide’s height to length ratio. Furthermore, we analyzed the present slope stability across the Skolis Mountain by using the landslide density (LD), landslide area percentage (LAP), and landslide frequency (LF). All these parameters were used to evaluate the spatial and temporal landslide distribution and evolution with the earthquake activity. These results can be considered as a powerful tool for earthquake-induced landslide disaster mitigation
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Kasahara, Natsuki, Yutaka Gonda, and Nejan Huvaj. "Quantitative Land-Use and Landslide Assessment: A Case Study in Rize, Türkiye." Water 14, no. 11 (June 4, 2022): 1811. http://dx.doi.org/10.3390/w14111811.
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Currently, many studies have reported that many landslides occur in tea or rubber plantation areas. In these areas, it is important to make a landslide susceptibility map and to take necessary measures to mitigate landslide damage. However, since historical landslide distribution data and land use data are not available, quantitative landslide assessment measurements have not been made in many countries. Therefore, in this study, landslide distribution maps and land use maps are created with worldwide available satellite imagery and Google Earth imagery, and the relationship between landslides and land use is analyzed in Rize, Türkiye. The results show that landslides are 1.75 to 5 times more likely to occur in tea gardens than in forests. It was also found that land use has the highest contribution to landslides among the landslide conditioning factors. The landslide assessment, using a simple landslide detection method and land use classification method with worldwide available data, enabled us to quantitatively reveal the characteristics of landslides. The results of this study reveal that quantitative landslide assessments can be applied in any location, where relatively high resolution satellite imagery and Google Earth imagery, or its alternatives, are available.
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Finlay, P. J., G. R. Mostyn, and R. Fell. "Landslide risk assessment: prediction of travel distance." Canadian Geotechnical Journal 36, no. 3 (October 25, 1999): 556–62. http://dx.doi.org/10.1139/t99-012.
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The Geotechnical Engineering Office of Hong Kong database of landslides for the period 1984-1993, containing over 3000 landslide records, and data for major landslides before this period were made available for a landslide risk assessment study. Data from over 1100 of these landslides in man-modified slopes were collated, processed, and analyzed statistically. Multiple regression models, based on slope geometry, were developed for the prediction of landslide travel on a horizontal surface below the slope. Although developed for slopes in Hong Kong, the models can be applied in similar geological situations (weathered granite and other weathered volcanics) and are suited for landslide risk zoning in areas below man-modified slopes and for the assessment of risks from individual cuts and fills.Key words: landslide, travel distance, risk, risk assessment.
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Guzzetti, F., M. Galli, P. Reichenbach, F. Ardizzone, and M. Cardinali. "Landslide hazard assessment in the Collazzone area, Umbria, Central Italy." Natural Hazards and Earth System Sciences 6, no. 1 (January 31, 2006): 115–31. http://dx.doi.org/10.5194/nhess-6-115-2006.
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Abstract. We present the results of the application of a recently proposed model to determine landslide hazard. The model predicts where landslides will occur, how frequently they will occur, and how large they will be in a given area. For the Collazzone area, in the central Italian Apennines, we prepared a multi-temporal inventory map through the interpretation of multiple sets of aerial photographs taken between 1941 and 1997 and field surveys conducted in the period between 1998 and 2004. We then partitioned the 79 square kilometres study area into 894 slope units, and obtained the probability of spatial occurrence of landslides by discriminant analysis of thematic variables, including morphology, lithology, structure and land use. For each slope unit, we computed the expected landslide recurrence by dividing the total number of landslide events inventoried in the terrain unit by the time span of the investigated period. Assuming landslide recurrence was constant, and adopting a Poisson probability model, we determined the exceedance probability of having one or more landslides in each slope unit, for different periods. We obtained the probability of landslide size, a proxy for landslide magnitude, by analysing the frequency-area statistics of landslides, obtained from the multi-temporal inventory map. Lastly, assuming independence, we determined landslide hazard for each slope unit as the joint probability of landslide size, of landslide temporal occurrence, and of landslide spatial occurrence.
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Dai, Cong, Weile Li, Huiyan Lu, and Shuai Zhang. "Landslide Hazard Assessment Method Considering the Deformation Factor: A Case Study of Zhouqu, Gansu Province, Northwest China." Remote Sensing 15, no. 3 (January 19, 2023): 596. http://dx.doi.org/10.3390/rs15030596.
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Landslides are geological disasters that can cause great damage to natural and social environments. Landslide hazard assessments are crucial for disaster prevention and mitigation. Conventional regional landslide hazard assessment results are static and do not take into account the dynamic changes in landslides; thus, areas with landslides that have been treated and stabilized are often still identified as high-risk areas. Therefore, a new hazard assessment method is proposed in this paper that combines the deformation rate results obtained by interferometric synthetic aperture radar (InSAR) with the results of conventional hazard assessments to obtain the hazard assessment level while considering the deformation factor of the study area, with Zhouqu, Gansu Province, selected as the case study. First, to obtain the latest landslide inventory map of Zhouqu, the hazard assessment results of the study area were obtained based on a neural network and statistical analysis, and an innovative combination of the deformation rate results of the steepest slope direction from the ascending and descending data were obtained by InSAR technology. Finally, the hazard assessment level considering the deformation factor of Zhouqu was obtained. The method proposed in this paper allows for a near-term hazard assessment of the study area, which in turn enables dynamic regional landslide hazard assessments and improves the efficiency of authorities when conducting high-risk-area identification and management.
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Poudel, Khagendra Raj, Ramesh Hamal, and Naresh Paudel. "Landslide Susceptibility Assessment: Identification and Hazard Mapping of Gandaki Province, Nepal." Prithvi Academic Journal 3 (June 21, 2020): 11–21. http://dx.doi.org/10.3126/paj.v3i0.29555.
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Landslides considered as a common hazard, affecting constantly the administrative territory of Gandaki province, located in the central part of Nepal. Impact of landslides is significant due to its specific geological, anthropic, vegetation and other circumstances. The main aim of this study was to identify the factors determining landslides and forming a landslide susceptibility mapping of study area. The fieldwork was conducted, where 128 GPS locations was recorded throughout the study area. This study also used the maximum entropy model using MaxEnt software, taking into account of various landslide-causing factors, resulting major variables of landslides risk and formed susceptibility mapping of landslide. It is identified that slope and land use land cover are most important variables to increase the landslide risk. Findings highlight that lands around the riversides and steep slopes are more risky area in terms of landslides. Moreover, it is found that the area of 3371.32 km2 measured as landslide risk zone in this province, where Gorkha district categorized as most vulnerable place for landslide, comprising of largest area of landslide risk zone while Parbat district has low amount of risk land. Since the human casualties and property loss are the major consequences of the disaster, it is essential to identify and analyse the factors determining for landslide and developing the landslide susceptibility mapping of Gandaki province, which could be taken into account while developing mitigation and coping strategies.
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Li, Langping, and Hengxing Lan. "Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study." International Journal of Environmental Research and Public Health 17, no. 21 (November 1, 2020): 8055. http://dx.doi.org/10.3390/ijerph17218055.
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Landslide spatial probability and size are two essential components of landslide susceptibility. However, in existing slope-unit-based landslide susceptibility assessment methods, landslide size has not been explicitly considered. This paper developed a novel slope-unit based approach for landslide susceptibility assessment that explicitly incorporates landslide size. This novel approach integrates the predicted occurrence probability (spatial probability) of landslides and predicted size (area) of potential landslides for a slope-unit to obtain a landslide susceptibility value for that slope-unit. The results of a case study showed that, from a quantitative point of view, integrating spatial probability and size in slope-unit-based landslide susceptibility assessment can bring remarkable increases of AUC (Area under the ROC curve) values. For slope-unit-based scenarios using the logistic regression method and the neural network method, the average increase of AUC brought by incorporating landslide size is up to 0.0627 and 0.0606, respectively. Slope-unit-based landslide susceptibility models incorporating landslide size had utilized the spatial extent information of historical landslides, which was dropped in models not incorporating landslide size, and therefore can make potential improvements. Nevertheless, additional case studies are still needed to further evaluate the applicability of the proposed approach.
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Kohno, Masanori, and Yuki Higuchi. "Landslide Susceptibility Assessment in the Japanese Archipelago Based on a Landslide Distribution Map." ISPRS International Journal of Geo-Information 12, no. 2 (January 22, 2023): 37. http://dx.doi.org/10.3390/ijgi12020037.
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Though danger prediction and countermeasures for landslides are important, it is fundamentally difficult to take preventive measures in all areas susceptible to dangerous landslides. Therefore, it is necessary to perform landslide susceptibility mapping, extract slopes with high landslide hazard/risk, and prioritize locations for conducting investigations and countermeasures. In this study, landslide susceptibility mapping along the whole slope of the Japanese archipelago was performed using the analytical hierarchy process (AHP) method, and geographic information system analysis was conducted to extract the slope that had the same level of hazard/risk as areas where landslides occurred in the past, based on the ancient landslide topography in the Japanese archipelago. The evaluation factors used were elevation, slope angle, slope type, flow accumulation, geology, and vegetation. The landslide susceptibility of the slope was evaluated using the score accumulation from the AHP method for these evaluation factors. Based on the landslide susceptibility level (I to V), a landslide susceptibility map was prepared, and landslide susceptibility assessment in the Japanese archipelago was identified. The obtained landslide susceptibility map showed good correspondence with the landslide distribution, and correlated well with past landslide occurrences. This suggests that our method can be applied to the extraction of unstable slopes, and is effective for prioritizing and implementing preventative measures.
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Shahi, Tapendra Kumar. "Earthquake-Induced Shallow Landslide Susceptibility Assessment of Gorkha District." Journal of Advanced College of Engineering and Management 5 (December 18, 2019): 181–93. http://dx.doi.org/10.3126/jacem.v5i0.26766.
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Nepal is very seriously affected by landslides every year causing loss of life and property. Large scale earthquakes that occurred in different time periods such as on 15th January, 1934 or that on 25th April 2015 have proved Nepal as seismically vulnerable -place. Nepal has witnessed several landslides during and after the earthquake events making some areas of land quite vulnerable for settlement and other usages. Therefore in order to minimize the impacts of landslides caused due to earthquakes, highly susceptible locations should be identified and spatial planning is made accordingly. Considering topographic effects in amplification of earthquake ground motion, Uchida et al. (2004) have developed a topographical parameter based empirical description of landslide susceptibility during an earthquake. In this research, the method proposed by Uchida et al. (2004) is utilized in raster GIS and landslide susceptibility analysis is performed in the study area of SulikotGaupalika of Gorkha district, Nepal which was severely hit by several landslides due to “Gorkha Earthquake 2015". The landslide inventory map of SulikotGaupalika due to “Gorkha Earthquake 2015" is obtained and is correlated with landslide susceptibility values as obtained by using Uchida et al. (2004). The analysis shows that the method proposed by Uchida et al. (2004) is more than 68.9% accurate in delineating the probable locations of earthquake induced landslides. By calibrating landslide data and landslide susceptibility values in a small site (i.e. SulikotGaupalika) within the study area, a final landslide susceptibility map is prepared for the whole study area of Gorkha district. The resultant susceptibility map is very useful for planning settlements, development activities and reconstruction planning.
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Chen, Qin, Lixia Chen, Lei Gui, Kunlong Yin, Dhruba Pikha Shrestha, Juan Du, and Xuelian Cao. "Assessment of the physical vulnerability of buildings affected by slow-moving landslides." Natural Hazards and Earth System Sciences 20, no. 9 (September 29, 2020): 2547–65. http://dx.doi.org/10.5194/nhess-20-2547-2020.
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Abstract. Physical vulnerability is a challenging and fundamental issue in landslide risk assessment. Previous studies mostly focus on generalized vulnerability assessment from landslides or other types of slope failures, such as debris flow and rockfall, while the long-term damage induced by slow-moving landslides is usually ignored. In this study, a method was proposed to construct physical vulnerability curves for masonry buildings by taking the Manjiapo landslide as an example. The landslide's force acting on the buildings' foundation is calculated by applying the landslide residual-thrust calculation method. Considering four rainfall scenarios, the buildings' physical responses to the thrust are simulated in terms of potential inclination by using Timoshenko's deep-beam theory. By assuming the landslide safety factor to be landslide intensity and inclination ratio to be vulnerability, a physical vulnerability curve is fitted and the relative function is constructed by applying a Weibull distribution function. To investigate the effects of buildings' parameters that influence vulnerabilities, the length, width, height, and foundation depth and Young's modulus of the foundation are analysed. The validation results on the case building show that the physical vulnerability function can give a good result in accordance with the investigation in the field. The results demonstrate that the building length, width, and foundation depth are the three most critical factors that affect the physical vulnerability value. Also, the result shows that the higher the ratio of length to width of the building, the more serious the damage to the building. Similarly, the shallower the foundation depth is, the more serious the damage will be. We hope that the established physical vulnerability curves can serve as tools for the quantitative risk assessment of slow-moving landslides.
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Wang, Zhiheng, Dongchuan Wang, Qiaozhen Guo, and Daikun Wang. "Regional landslide hazard assessment through integrating susceptibility index and rainfall process." Natural Hazards 104, no. 3 (September 5, 2020): 2153–73. http://dx.doi.org/10.1007/s11069-020-04265-5.
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Abstract Due to the difference of the spatial and temporal distribution of rainfall and the complex diversity of the disaster-prone environment (topography, geological, fault, and lithology), it is difficult to assess the hazard of landslides at the regional scale quantitatively only considering rainfall condition. Based on detailed landslide inventory and rainfall data in the hilly area in Sichuan province, this study analyzed the effects of both rainfall process and environmental factors on the occurrence of landslides. Through analyzing environmental factors, a landslide susceptibility index (LSI) was calculated using multiple layer perceptron (MLP) model to reflect the regional landslide susceptibility. Further, the characteristics of rainfall process and landslides were examined quantitatively with statistical analysis. Finally, a probability model integrating LSI and rainfall process was constructed using logistical regression analysis to assess the landslide hazard. Validation showed satisfactory results, and the inclusion of LSI effectively improved the accuracy of the landslide hazard assessment: Compared with only considering the rainfall process factors, the accuracy of the landslide prediction model both considering the rainfall process and landslide susceptibility is improved by 3%. These results indicate that an integration of susceptibility index and rainfall process is essential in improving the timeliness and accuracy of regional landslide early warning.
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Kawagoe, S., S. Kazama, and P. R. Sarukkalige. "Probabilistic modelling of rainfall induced landslide hazard assessment." Hydrology and Earth System Sciences 14, no. 6 (June 25, 2010): 1047–61. http://dx.doi.org/10.5194/hess-14-1047-2010.
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Abstract. To evaluate the frequency and distribution of landslides hazards over Japan, this study uses a probabilistic model based on multiple logistic regression analysis. Study particular concerns several important physical parameters such as hydraulic parameters, geographical parameters and the geological parameters which are considered to be influential in the occurrence of landslides. Sensitivity analysis confirmed that hydrological parameter (hydraulic gradient) is the most influential factor in the occurrence of landslides. Therefore, the hydraulic gradient is used as the main hydraulic parameter; dynamic factor which includes the effect of heavy rainfall and their return period. Using the constructed spatial data-sets, a multiple logistic regression model is applied and landslide hazard probability maps are produced showing the spatial-temporal distribution of landslide hazard probability over Japan. To represent the landslide hazard in different temporal scales, extreme precipitation in 5 years, 30 years, and 100 years return periods are used for the evaluation. The results show that the highest landslide hazard probability exists in the mountain ranges on the western side of Japan (Japan Sea side), including the Hida and Kiso, Iide and the Asahi mountainous range, the south side of Chugoku mountainous range, the south side of Kyusu mountainous and the Dewa mountainous range and the Hokuriku region. The developed landslide hazard probability maps in this study will assist authorities, policy makers and decision makers, who are responsible for infrastructural planning and development, as they can identify landslide-susceptible areas and thus decrease landslide damage through proper preparation.
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Boyko, Kateryna. "Application of a complex assessment of landslide hazards in mountain regions." Przegląd Naukowy Inżynieria i Kształtowanie Środowiska 26, no. 3 (September 15, 2017): 287–97. http://dx.doi.org/10.22630/pniks.2017.26.3.28.
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The main regional factors of occurrence and activation of landslides within the mountain region were examined. As a result of study of recommendations made by experts, geologists, and gap analysis of existing methods of forecasting the landslide process, an algorithm of comprehensive assessment of landslide hazard areas based on the construction of models in a GIS environment was proposed. These models describe the spatial patterns of landslides. All factors determining the tendency of the studies area to the landslide process development were divided into actual factors, reflecting the regional peculiarities of the territory and forming the landslide-prone slopes (static model), as well as triggering factors, initiating the landslide process and determining its activity (dynamic model). The first cartographic model was built, showing the distribution of the deterministic indirect indicator of landslide hazard, i.e. stability index.
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Kocaman, S., and C. Gokceoglu. "POSSIBLE CONTRIBUTIONS OF CITIZEN SCIENCE FOR LANDSLIDE HAZARD ASSESSMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W4 (March 6, 2018): 295–300. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w4-295-2018.
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<p><strong>Abstract.</strong> Landslide is perhaps one of the most complex natural phenomena and is quite common throughout the World. Before the human appearance on the World, it was only an earth surface process, whereas it became one of the most destructive natural hazards with the anthropogenic activities and the increase in human population. Landslides cause serious harmful and destructive effects on roads, railways, buildings, infrastructures, lifelines, quality of surface waters, etc. To reduce the losses caused by landslides, high quality landslide susceptibility and hazard maps are crucial. With the recent technological developments, the quality of regional landslide susceptibility and hazard assessments has been increased. Preparation of a complete landslide inventory map with accurate temporal dimension can be extremely difficult, or even impossible. Inaccurate and incomplete temporal landslide inventory maps result in serious uncertainties on the assessment results of regional landslide hazard. Therefore, lack of timely accurate data is the main source of problem affecting quality of the regional landslide assessments.<br> With the emerging developments in geospatial technologies, as well as the transforming power of information and communication technologies (ICT) on the society, it became possible to use the citizen science methods in scientific processes, which has enormous potential in landslide data collection and thus reduce the losses. The main aim of this review is to discuss the uncertainties lead by missing data and affecting quality of regional landslide assessments, and to describe the potential of citizen science to reduce the uncertainties. For this purpose, a brief review on the landslide susceptibility and hazard studies have been performed and the sources of uncertainties have been described. Finally, the role of citizen science is discussed with specific examples. As a final conclusion drawn from the present study, it is possible to say that citizen science may provide substantial contribution on the quality of regional landslide assessments.</p>
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Roccati, Anna, Guido Paliaga, Fabio Luino, Francesco Faccini, and Laura Turconi. "GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment." Land 10, no. 2 (February 5, 2021): 162. http://dx.doi.org/10.3390/land10020162.
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Landslide susceptibility mapping is essential for a suitable land use managing and risk assessment. In this work a GIS-based approach has been proposed to map landslide susceptibility in the Portofino promontory, a Mediterranean area that is periodically hit by intense rain events that induce often shallow landslides. Based on over 110 years landslides inventory and experts’ judgements, a semi-quantitative analytical hierarchy process (AHP) method has been applied to assess the role of nine landslide conditioning factors, which include both natural and anthropogenic elements. A separated subset of landslide data has been used to validate the map. Our findings reveal that areas where possible future landslides may occur are larger than those identified in the actual official map adopted in land use and risk management. The way the new map has been compiled seems more oriented towards the possible future landslide scenario, rather than weighting with higher importance the existing landslides as in the current model. The paper provides a useful decision support tool to implement risk mitigation strategies and to better apply land use planning. Allowing to modify factors in order to local features, the proposed methodology may be adopted in different conditions or geographical context featured by rainfall induced landslide risk.
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Kawagoe, S., S. Kazama, and P. R. Sarukkalige. "Probabilistic modelling of rainfall induced landslide hazard assessment." Hydrology and Earth System Sciences Discussions 7, no. 1 (January 29, 2010): 725–66. http://dx.doi.org/10.5194/hessd-7-725-2010.
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Abstract. To evaluate the frequency and distribution of landslides hazards over Japan, this study uses a probabilistic model based on multiple logistic regression analysis. Study particular concerns several important physical parameters such as hydraulic parameters, geographical parameters and the geological parameters which are considered to be influential in the occurrence of landslides. Sensitivity analysis confirmed that hydrological parameter (hydraulic gradient) is the most influential factor in the occurrence of landslides. Therefore, the hydraulic gradient is used as the main hydraulic parameter; dynamic factor which includes the effect of heavy rainfall and their return period. Using the constructed spatial data-sets, a multiple logistic regression model is applied and landslide susceptibility maps are produced showing the spatial-temporal distribution of landslide hazard susceptibility over Japan. To represent the susceptibility in different temporal scales, extreme precipitation in 5 years, 30 years, and 100 years return periods are used for the evaluation. The results show that the highest landslide hazard susceptibility exists in the mountain ranges on the western side of Japan (Japan Sea side), including the Hida and Kiso, Iide and the Asahi mountainous range, the south side of Chugoku mountainous range, the south side of Kyusu mountainous and the Dewa mountainous range and the Hokuriku region. The developed landslide hazard susceptibility maps in this study will assist authorities, policy makers and decision makers, who are responsible for infrastructural planning and development, as they can identify landslide-susceptible areas and thus decrease landslide damage through proper preparation.
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Gerzsenyi, Dávid. "FRMOD, a Python tool for statistical landslide susceptibility assessment." Advances in Cartography and GIScience of the ICA 3 (November 29, 2021): 1–6. http://dx.doi.org/10.5194/ica-adv-3-4-2021.
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Abstract. Locating landslide-prone slopes is important, as landslides often threaten life or property where they occur. There is an abundance of statistical methods in the literature for estimating susceptibility to landslides, i.e., the likelihood of landslide occurrence based on the analyzed conditions. Still, there is a lack of readily available GIS tools for landslide susceptibility analysis, making it hard to reproduce or compare the results of different susceptibility assessments. The FRMOD is a Python-based tool for conducting landslide susceptibility analysis with the frequency ratio method. The frequency ratio method yields susceptibility estimates by comparing the frequency distributions of a set of variables from the sample landslide areas to the distributions for the whole study area. The estimates show the level of similarity to the sample landslides. The two main inputs of the tool are the raster grids of the analyzed continuous (e.g., elevation, slope) and thematic (e.g., lithology) variables and the mask grid that marks the landslide and the non-landslide areas. The analysis is performed with cross-validation to measure the predictive performance of the model. Data computed during the analysis is stored along the final susceptibility estimates and the supplementary statistics. The script reads and writes GDAL-compatible rasters, while the statistics can be saved as text files. Basic plotting functionalities for the grids and the statistics are also built-in to quicken the evaluation of the results. FRMOD enables the swift testing of different analysis setups and to apply the same analysis method for different areas with relative ease.
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Filipović, Marina, Ivan Mišur, Vlatko Gulam, and Marija Horvat. "A case study in the research polygon in Glina and Dvor municipality, Croatia–landslide susceptibility assessment of geological units." Geologia Croatica 75, no. 1 (February 28, 2022): 17–33. http://dx.doi.org/10.4154/gc.2022.04.
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In this paper, a preliminary analysis of the landslide inventory is presented for the wider area of the municipalities of Glina and Dvor, within Sisak-Moslavina County in Croatia, where LiDAR scanning for 45.85 km2 was conducted. Landslide polygons were outlined based on the visual interpretation of HRDEM derivates. In total, 477 landslides were contoured with an average landslide density of 9.85 per km2. Most of the landslides are characterised as moderate, shallow, and not recent. The spatial relationship between landslides and geological units is expressed with the landslide index. Subsequently, the geological units were grouped into four engineering geological units representing different susceptibilities to landslides. The geological units most prone to landslides are the Eocene, Oligocene, Palaeocene and Jurassic sandstones. Even though all geological units were analysed here, the majority of landslides are within sandstones. A particular emphasis was on landslide occurrence in metamorphic and igneous rocks of the ophiolite sequence, a distinctive characteristic of the research area where less susceptibility to landslide processes was observed. Moreover, to further distinguish the differences between the units in the area a morphometric characteristic (relief) and drainage network was also analysed. The purpose of this analysis was to additionally confirm the landslide susceptibility assessment and the division of geological units into engineering geological units, which again implied the different behaviours between landslides in igneous and metamorphic rocks compared to sandstones. Because the research area is poorly studied regarding landslide susceptibility, relief, and drainage networks, these findings will be a step forward in recognising the relationship between them and creating a base for the development of a landslide susceptibility map for this area.
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Su, Xiaojun, Yi Zhang, Xingmin Meng, Mohib Ur Rehman, Zainab Khalid, and Dongxia Yue. "Updating Inventory, Deformation, and Development Characteristics of Landslides in Hunza Valley, NW Karakoram, Pakistan by SBAS-InSAR." Remote Sensing 14, no. 19 (September 30, 2022): 4907. http://dx.doi.org/10.3390/rs14194907.
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The Hunza Valley, in the northwestern Karakoram Mountains, North Pakistan, is a typical region with many towns and villages, and a dense population and is prone to landslides. The present study completed landslide identification, updating a comprehensive landslide inventory and analysis. First, the ground surface deformation was detected in the Hunza Valley by SBAS-InSAR from ascending and descending datasets, respectively. Then, the locations and boundaries were interpreted and delineated, and a comprehensive inventory of 118 landslides, including the 53 most recent InSAR identified active landslides and 65 landslides cited from the literature, was completed. This study firstly named all 118 landslides, considering the demand for globally intensive research and hazard mitigation. Finally, the deformation, spatial–topographic development, and distribution characteristics in the Hunza Valley scale and three large significant landslides were analyzed. Information on 72 reported landslides was used to construct an empirical power law relationship linking landslide area (AL) to volume (VL) (VL = 0.067 × AL1.351), and this formula predicted the volume of 118 landslides in this study. We discovered that the landslides from the literature, which were interpreted from optical images, had lower levels of velocity, area, elevation, and height. The SBAS-InSAR-detected active landslide was characterized by higher velocity, larger area, higher elevation, larger slope gradient, larger NDVI (normalized difference vegetation index), and greater height. The melting glacier water and rainfall infiltration from cracks on the landslide’s upper part may promote the action of a push from gravity on the upper part. Simultaneously, the coupling of actions from river erosion and active tectonics could have an impact on the stability of the slope toe. The up-to-date comprehensive identification and understanding of the characteristics and mechanism of landslide development in this study provide a reference for the next step in landslide disaster prevention and risk assessment.
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Muminov, B. Kh, I. K. Fomenko, and O. N. Sirotkina. "Landslide hazard assessment of the Norak region of Tadjikistan." Proceedings of higher educational establishments. Geology and Exploration, no. 1 (June 22, 2022): 50–60. http://dx.doi.org/10.32454/0016-7762-2022-64-1-50-60.
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Introduction. The application of geographic information system (GIS) technologies is a promising technology for mapping and evaluating landslide hazards. At present, satellite-based terrestrial sensing is widely used. Satellite images make it possible not only to identify individual landslides, particularly in hard-to-reach areas, but also to identify those locations where landslide processes have manifested, are manifesting and may manifest themselves in the future.Aim. Mapping of the present landslide propagation in the territory of the Norak region of Tajikistan using satellite imaging and GIS.Materials and methods. In the course of the study, maps of landslide propagation were compiled both automatically and manually. An analysis of the results of previous studies and interpretations of satellite images was conducted. Digital models of the relief, as well as topographic, geomorphological, engineering and geological maps, were investigated.Results. A detailed map of landslide phenomena was compiled in the GIS format and a database of landslide phenomena was created for the first time for the territory of the Norak region of the Republic of Tajikistan.Conclusions. The conducted zoning showed that, out of the total landslide number, 24%, 36%, 10%, 18% and 12% correspond to very small, small, medium, large, and very large landslides, respectively. The landslide processes are developed across the area of 2601×104 m2, which accounts for the 4% of the total area of the district. The results demonstrate that the approach used in this study may be applicable to the landslide hazard assessment in hard-to-reach and poorly studied regions.
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Song, Chang-Ho, Ji-Sung Lee, Yong-Soo Ha, and Yun-Tae Kim. "Rainfall and Earthquake-induced Landslide Susceptibility Assessment." Journal of the Korean Society of Hazard Mitigation 23, no. 1 (February 28, 2023): 165–77. http://dx.doi.org/10.9798/kosham.2023.23.1.165.
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When rainfall and earthquake occur simultaneously, large-scale landslides can cause direct damage to lives and structures. To reduce such large-scale landslide damage, rainfall and earthquake-induced landslide susceptibility has to be evaluated. Therefore, in this study, landslide susceptibility was evaluated by considering only rainfall, only earthquake, and both rainfall and earthquake in the Atsuma region of Japan, where a large-scale landslide was triggered by a combination of rainfall and earthquake. Through quantitative index analysis, the accuracy of the landslide susceptibility map was improved when both rainfall and earthquake were considered. The proposed method was also applied to the Naari basin in Yangnam-myeon, Gyeongju as a pilot district. We found that the landslide-susceptible area increased when rainfall and earthquake were considered simultaneously than when rainfall and earthquake were considered separately. Through such research, can be used as basic data for complex disaster vulnerability assessment.
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Ercanoglu, M. "Landslide susceptibility assessment of SE Bartin (West Black Sea region, Turkey) by artificial neural networks." Natural Hazards and Earth System Sciences 5, no. 6 (December 5, 2005): 979–92. http://dx.doi.org/10.5194/nhess-5-979-2005.
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Abstract. Landslides are significant natural hazards in Turkey, second only to earthquakes with respect to economic losses and casualties. The West Black Sea region of Turkey is known as one of the most landslide-prone regions in the country. The work presented in this paper is aimed at evaluating landslide susceptibility in a selected area in the West Black Sea region using Artificial Neural Network (ANN) method. A total of 317 landslides were identified and mapped in the area by extensive field work and by use of air photo interpretations to build a landslide inventory map. A landslide database was then derived automatically from the landslide inventory map. To evaluate landslide susceptibility, six input parameters (slope angle, slope aspect, topographical elevation, topographical shape, wetness index, and vegetation index) were used. To obtain maps of these parameters, Digital Elevation Model (DEM) and ASTER satellite imagery of the study area were used. At the first stage, all data were normalized in [0, 1] interval, and parameter effects on landslide occurrence were expressed using Statistical Index values (Wi). Then, landslide susceptibility analyses were performed using an ANN. Finally, performance of the resulting map and the applied methodology is discussed relative to performance indicators, such as predicted areal extent of landslides and the strength of relation (rij) value. Much of the areal extents of the landslides (87.2%) were classified as susceptible to landsliding, and rij value of 0.85 showed a high degree of similarity. In addition to these, at the final stage, an independent validation strategy was followed by dividing the landslide data set into two parts and 82.5% of the validation data set was found to be correctly classified as landslide susceptible areas. According to these results, it is concluded that the map produced by the ANN is reliable and methodology applied in the study produced high performance, and satisfactory results.
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Maes, Jan, Matthieu Kervyn, Astrid de Hontheim, Olivier Dewitte, Liesbet Jacobs, Kewan Mertens, Matthias Vanmaercke, Liesbet Vranken, and Jean Poesen. "Landslide risk reduction measures: A review of practices and challenges for the tropics." Progress in Physical Geography: Earth and Environment 41, no. 2 (February 8, 2017): 191–221. http://dx.doi.org/10.1177/0309133316689344.
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The overall objective of this review is to gain insights into landslide risk reduction measures that are applied or recommended in tropical landslide-prone countries, and the challenges at play. More specifically, this review aims to (i) presenting an overview of recent studies on landslides and landslide risk reduction in these countries, (ii) exploring the factors controlling the publication output on landslides and landslide risk reduction, (iii) reviewing the various landslide risk reduction measures recommended and implemented, and (iv) identifying the bottlenecks for the implementation of these strategies. A compilation of recommended and implemented landslide risk reduction measures in 99 landslide-prone tropical countries was made, based on an extensive review of scientific literature (382 publications). The documented measures are analysed using a scheme of risk reduction measures that combines classifications of the Hyogo Framework for Action and the SafeLand project. Our literature review shows that the factors influencing the number of publications on landslides and landslide risk reduction per country are (in order of importance) the absolute physical exposure of people to landslides, the population number and the Human Development Index of a country. The ratio of publications on landslide risk reduction versus publications on landslides for landslide-prone tropical countries does not vary much between these countries (average: 0.28). A significant fraction (0.30) of all known landslide hazard reduction measures are neither implemented nor recommended according to our review. The most recommended landslide risk reduction component is ‘risk management and vulnerability reduction’ (0.38). However, the most implemented component is ‘risk assessment’ (0.57). Overall, the ratio of implemented versus recommended landslide risk reduction measures in the tropics is low (<0.50) for most landslide risk reduction components, except for ‘risk assessment’ (3.01). The most cited bottlenecks for implementing landslide risk reduction measures are scientific (0.30) and political (0.29) in nature.
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Kasiyanchuk, Dmytro, and Liudmyla Shtohryn. "Assessment of the Ecological Risks of Landslide Damages in the Carpathian Region." Grassroots Journal of Natural Resources 4, no. 3 (September 30, 2021): 52–61. http://dx.doi.org/10.33002/nr2581.6853.040306.
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The dynamism of the landslides within the Carpathian region of Ukraine is because of the difficult engineering and geological conditions. High landslide den sity and significant population density contribute to the fact that environmental parameters worsen and require rational management. Permanent natural factors like clay flysch formation, fault tectonics, high seismic activity, and dense network of rivers mostly facilitate the active development of landslides in the Carpathian region. However, it is triggered by extreme long-term precipitation. The numerical parameters of population density, the landslide damage coefficient, and the predictive range of landslide intensification were selected to assess the ecological risk of damages in the area. The landslide dam age coefficient characterizes the tendency of the area to landslide development, considering all the factors contributing to the landslides. Risk, as a multifunctional calculated complex, includes the calculation of damage, according to which we can assess the possibility of risk for the human being while assuming the equal distribution of the population within the study area. The integral components of the risk are calculated based on the data gathered to assess the growth of risks in the future, considering the area distribution and predictive time series of the landslide intensification. This analysis has identified engineering and geological areas having the greatest risk to human life.
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Ahmad, Sofia. "Study on Advance Technique and Recent Trends for Landslide Hazard Assessment." International Journal of Advance Research and Innovation 2, no. 1 (2014): 255–62. http://dx.doi.org/10.51976/ijari.211439.
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Landslides are the most far-flung natural hazards, causing loss of thousands of lives and billions of dollars annually worldwide. Particularly in the hilly regions has led to the necessity of in-depth study and research in the field of landslide hazard zonation. Despite many technical papers being considered in this area of study, there is no particular standard method available for evaluating and predicting the pattern of landslides. Different researchers come up with different methods for landslide hazard assessment, due to complexity involved in the landslide triggering mechanisms. As the triggering factors of the landslides are not uniform and the nature of the earth is not same, it is very often confusing to identify appropriate method to apply. This paper deals with the compilation of various landslide hazard assessment methods with adequate contextual information. A critical review is presented on each of these methods, highlighting their limitations and suitability of application.
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Wu, Wenhuan, Qiang Zhang, Vijay P. Singh, Gang Wang, Jiaqi Zhao, Zexi Shen, and Shuai Sun. "A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai–Tibetan Plateau." Remote Sensing 14, no. 18 (September 19, 2022): 4662. http://dx.doi.org/10.3390/rs14184662.
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Amplifying landslide hazards in the backdrop of warming climate and intensifying human activities calls for an integrated framework for accurately evaluating landslide susceptibility at fine spatiotemporal resolutions, which is critical for the mitigation of increasingly high landslide disaster risks. Yet, dynamic landslide susceptibility mapping is still lacking. Using high-quality data, from 14,435 landslides and non-landslides, we developed an efficient holistic framework for evaluating landslide susceptibility, considering landslide-relevant internal and external factors based on cloud computing platform and algorithmic models, which enables dynamic updating of a landslide susceptibility map at the regional scale, particularly in regions with highly complicated topographical features such as the Hengduan Mountains, as considered in this study. We compared Classification and Regression Trees (CART), Support Vector Machines (SVM), and Random Forest (RF) classifiers to screen out the best portfolio model for landslide susceptibility mapping on the Google Earth Engine (GEE) platform. We found that the Random Forest (RF) classifier integrated with synergy mode had the best modeling performance, with 90.48% and 89.24% accuracy and precision, respectively. We also found that forests and grasslands had the controlling effect on the occurrence of landslides, while human activities had a notable inducing effect on the occurrence of landslides within the Hengduan Mountains. This study highlights the performance of the holistic landslide susceptibility evaluation framework proposed in this study and provides a viable technique for landslide susceptibility evaluation in other regions of the globe.
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Dikshit, Abhirup, Raju Sarkar, Biswajeet Pradhan, Saroj Acharya, and Abdullah M. Alamri. "Spatial Landslide Risk Assessment at Phuentsholing, Bhutan." Geosciences 10, no. 4 (April 7, 2020): 131. http://dx.doi.org/10.3390/geosciences10040131.
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Landslides are one of the most destructive and most recurring natural calamities in the Himalayan region. Their occurrence leads to immense damage to infrastructure and loss of land, human lives, and livestock. One of the most affected regions is the Bhutan Himalayas, where the majority of the landslides are rainfall-induced. The present study aims to determine the hazard and risk associated with rainfall-induced landslides for the Phuentsholing region located in the southwestern part of the Bhutan Himalayas. The work involves developing a landslide risk map using hazard and vulnerability maps utilizing landslide records from 2004 to 2014. The landslide hazard map was generated by determining spatial and temporal probabilities for the study region. The spatial probability was computed by analyzing the landslide contributing factors like geology, slope, elevation, rainfall, and vegetation based on comprehensive field study and expertise about the area. The contributing factors were divided into various classes and the percentage of landslide occurrence under each class was calculated to understand its contributing significance. Thereafter, a weighted linear combination approach was used in a GIS environment to develop the spatial probability map which was multiplied with temporal probabilities based on regional rainfall thresholds already determined for the region. Consequently, vulnerability assessment was conducted using key elements at risk (population, land use/land cover, proximity to road, proximity to stream) and the weights were provided based on expert judgment and comprehensive field study. Finally, risk was determined and the various regions in the study area were categorized as high, medium, and low risk. Such a study is necessary for low-economic countries like Bhutan which suffers from unavailability of extensive data and research. The study is conducted for a specific region but can be extended to other areas around the investigated area. The tool can serve as an indicator for the civil authorities to analyze the risk posed by landslides due to the rapid infrastructure development in the region.
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Liu, Wangcai, Yi Zhang, Yiwen Liang, Pingping Sun, Yuanxi Li, Xiaojun Su, Aijie Wang, and Xingmin Meng. "Landslide Risk Assessment Using a Combined Approach Based on InSAR and Random Forest." Remote Sensing 14, no. 9 (April 29, 2022): 2131. http://dx.doi.org/10.3390/rs14092131.
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Landslide risk assessment is important for risk management and loss–damage reduction. Herein, we assessed landslide susceptibility, hazard, and risk in the urban area of Yan’an City, which is located on the Loess Plateau of China and affected by many loess landslides. Based on 1841 slope units mapped in the study area, a random forest machine learning classifier and eight environmental factors influencing landslides were used for a landslide susceptibility assessment. In addition, differential synthetic aperture radar interferometry (DInSAR) technology was used for a hazard assessment. The accuracy of the random forest is 0.903 and the area under the receiver operating characteristics (ROC) curve is 0.96. The results show that 16% and 22% of the slope units were classified as being at very high and high-susceptibility levels for landslides, respectively, whereas 16% and 24% of the slope units were at very high and high-hazard levels for landslides, respectively. The landslide risk was obtained based on the susceptibility map and hazard map of landslides. The results show that only 26% of the slope units were located at very high and high-risk levels for landslides and these are mainly concentrated in urban centers. Such risk zones should be taken seriously and their dynamics must be monitored. Our landslide risk map is expected to provide information for planners to help them choose appropriate locations for development schemes and improve integrated geohazard mitigation in Yan’an City.
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Sui, Haoyue, Tianming Su, Ruilin Hu, Dong Wang, and Zhengwei Zheng. "Study on the Risk Assessment Method of Rainfall Landslide." Water 14, no. 22 (November 14, 2022): 3678. http://dx.doi.org/10.3390/w14223678.
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Quantitative risk assessment of landslides has always been the focus and difficulty in the field of landslide research. In this paper, taking Mayang County, Hunan Province as an example, the risk assessment of rainfall-induced landslides was carried out from the regional and individual scales. On the regional scale, the risk factors of geological disasters were analyzed. Based on the slope unit, the risk analysis of slope geological disasters and the vulnerability risk assessment of hazard-bearing bodies were carried out to form the block plan. On an individual scale, based on the analysis of rainfall extreme value, the variation law of landslide seepage field and stability under different rainfall recurrence periods was simulated. Then, the vulnerability of the disaster-bearing body was studied according to the analysis of the impact range and the field investigation. Combined with the evaluation results of landslide hazard and vulnerability of the disaster-bearing body, the life and economic risks under different working conditions were further obtained. Therefore, the research results could provide not only a reference for the risk assessment of rainfall-induced landslides in other regions but also a theoretical basis for the early warning and prediction of landslide disasters.
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Siakian, Varoujan K., Sarhang A. Omer, and Diyar Salah M.Khalil. "Assessment of Dukan Landslides, West of Sulaimani City, the Kurdistan Region, Northeast Iraq." UKH Journal of Science and Engineering 2, no. 1 (June 30, 2018): 28–38. http://dx.doi.org/10.25079/ukhjse.v2n1y2018.pp28-38.
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Four landslides of different sizes had occurred in 2012 along the main paved road between Dukan – Surdash. The landslides are within the Kometan Formation, which consist mainly of well bedded and jointed marly limestone. The landslides are located in a narrow syncline trending NW – SE in between Khalikan and Kalo anticlines. The main reason of the landslides is the haphazard road cuts during the road construction and widening of the road latter on. Another reason that had accelerated the landslide is the fluctuation of water level in Dukan reservoir. The water level reaches the down slope area of the landslides. Satellite imagery was used to study the parameters of the landslides and have performed a field work to confirm the acquired parameters and deduced other indications of the landslides, besides checking their stability. Landslide Possibility Study was performed using Landslide Possibility Index at each landslide area and have found that the whole area is unstable showing High possibility of re-failure. Sereographic presentation for the three landslides was used to show the relation between bedding and joint planes as well the road cuts trends. It was concluded that in next rainy season, the whole area may form a severe geological hazard if necessary precautions are not considered. Therefore, urgent precautions were recommended to keep the slopes alongside the road cur as stable as possible.
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Li, Y., G. Chen, C. Tang, G. Zhou, and L. Zheng. "Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network." Natural Hazards and Earth System Sciences 12, no. 8 (August 31, 2012): 2719–29. http://dx.doi.org/10.5194/nhess-12-2719-2012.
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Abstract. A GIS-based method for the assessment of landslide susceptibility in a selected area of Qingchuan County in China is proposed by using the back-propagation Artificial Neural Network model (ANN). Landslide inventory was derived from field investigation and aerial photo interpretation. 473 landslides occurred before the Wenchuan earthquake (which were thought as rainfall-induced landslides (RIL) in this study), and 885 earthquake-induced landslides (EIL) were recorded into the landslide inventory map. To understand the different impacts of rainfall and earthquake on landslide occurrence, we first compared the variations between landslide spatial distribution and conditioning factors. Then, we compared the weight variation of each conditioning factor derived by adjusting ANN structure and factors combination respectively. Last, the weight of each factor derived from the best prediction model was applied to the entire study area to produce landslide susceptibility maps. Results show that slope gradient has the highest weight for landslide susceptibility mapping for both RIL and EIL. The RIL model built with four different factors (slope gradient, elevation, slope height and distance to the stream) shows the best success rate of 93%; the EIL model built with five different factors (slope gradient, elevation, slope height, distance to the stream and distance to the fault) has the best success rate of 98%. Furthermore, the EIL data was used to verify the RIL model and the success rate is 92%; the RIL data was used to verify the EIL model and the success rate is 53%.
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Williams, Jack G., Nick J. Rosser, Mark E. Kincey, Jessica Benjamin, Katie J. Oven, Alexander L. Densmore, David G. Milledge, Tom R. Robinson, Colm A. Jordan, and Tom A. Dijkstra. "Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes." Natural Hazards and Earth System Sciences 18, no. 1 (January 16, 2018): 185–205. http://dx.doi.org/10.5194/nhess-18-185-2018.
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Abstract. Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 Mw 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response.
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Shah, Nisar Ali, Muhammad Shafique, Muhammad Ishfaq, Kamil Faisal, and Mark Van der Meijde. "Integrated Approach for Landslide Risk Assessment Using Geoinformation Tools and Field Data in Hindukush Mountain Ranges, Northern Pakistan." Sustainability 15, no. 4 (February 8, 2023): 3102. http://dx.doi.org/10.3390/su15043102.
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Landslides are one of the most recurring and damaging natural hazards worldwide, with rising impacts on communities, infrastructure, and the environment. Landslide hazard, vulnerability, and risk assessments are critical for landslide mitigation, land use and developmental planning. They are, however, often lacking in complex and data-poor regions. This study proposes an integrated approach to evaluate landslide hazard, vulnerability, and risk using a range of freely available geospatial data and semi-quantitative techniques for one of the most landslide-prone areas in the Hindukush mountain ranges of northern Pakistan. Very high-resolution satellite images and their spectral characteristics are utilized to develop a comprehensive landslide inventory and predisposing factors using bi-variate models to develop a landslide susceptibility map. This is subsequently integrated with landslide-triggering factors to derive a Landslide Hazard Index map. A geospatial database of the element-at-risk data is developed from the acquired remote sensing data and extensive field surveys. It contains the building’s footprints, accompanied by typological data, road network, population, and land cover. Subsequently, it is analyzed using a spatial multi-criteria evaluation technique for vulnerability assessment and further applied in a semi-quantitative technique for risk assessment in relative risk classes. The landslide risk assessment map is classified into five classes, i.e., very low (18%), low (39.4%), moderate (26.3%), high (13.3%), and very high (3%). The developed landslide risk index map shall assist in highlighting the landslide risk hotspots and their subsequent mitigation and risk reduction.
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Sinčić, Marko, Sanja Bernat Gazibara, Martin Krkač, and Snježana Mihalić Arbanas. "Landslide susceptibility assessment of the City of Karlovac using the bivariate statistical analysis." Rudarsko-geološko-naftni zbornik 38, no. 2 (2022): 149–70. http://dx.doi.org/10.17794/rgn.2022.2.13.
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A preliminary landslide susceptibility analysis on a regional scale of 1:100 000 using bivariate statistics was conducted for the City of Karlovac. The City administration compiled landslide inventory used in the analysis based on recorded landslides from 2014 to 2019 that caused significant damage to buildings or infrastructures. Analyses included 17 geofactors relevant to landslide occurrence and classified them into four groups: geomorphological (elevation, slope gradient, slope orientation, terrain curvature, terrain roughness), geological (lithology-rock type, proximity to geological contacts, proximity to faults), hydrological (proximity to drainage network, proximity to springs, proximity to temporary, permanent and to all streams, topographic wetness) and anthropogenic (proximity to traffic infrastructure, land cover using two classifications). Five scenarios were defined using a different combination of geofactors weighted by the Weights-of-Evidence (WoE) method, resulting in five different landslide susceptibility maps. The best landslide susceptibility map was selected upon the results of a ROC curve analysis, which was used to obtain success and prediction rates of each scenario. The novelty in the presented research is that a limited amount of thematic data and an incomplete landslide inventory map allows for the production of a preliminary landslide susceptibility map for usage in spatial planning. Also, this study provides a discussion regarding the used method, geofactors, defined scenarios and reliability of the results. The final preliminary landslide susceptibility map was derived using ten geofactors, which satisfied the pairwise CI test, and it is classified in four zones: low landslide susceptibility (57.05% of the area), medium landslide susceptibility (20.63% of the area), high landslide susceptibility (13.28% of the area), and very high landslide susceptibility (9.03% of the area), and has a success rate of 94% and a prediction rate of 93% making it a highly accurate source of preliminary information for the study area.
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Wang, J. M., A. D. Gong, Y. L. Chen, J. Li, and T. T. Zeng. "HAZARD ASSESSMENT OF LANDSLIDE DISASTER IN FUJIAN PROVINCE BASED ON FUZZY MATHEMATICS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1733–38. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1733-2018.
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Landslide disasters are the most frequent geological disasters in Fujian Province. They are also the type of geological disasters that cause the most serious economic and population losses each year. This study uses the fuzzy mathematics method to carry on the hazard assessment of landslide disaster in Fujian Province, in order to explore the mechanism of the landslide disaster, and provides the reference for the construction land expansion in Fujian Province. The calculation results show that landslide disasters in Fujian Province have a high correlation with external forces, that is, long-term precipitation and short-term strong precipitation are likely to trigger landslide disasters. Among the internal stress factors, the correlation between the occurrence of landslides and slopes is the highest, and the probability of landslides occurs in areas with steep slopes. The evaluation results show that the areas with high landslide hazards are mainly distributed in the central region of Fujian Province. From the remote sensing images, it can be seen that most of the dangerous areas are in rapidly developing cities, and their vegetation coverage is relatively low, and the environment is greatly affected by humans.
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Niraj Baral, Akhilesh Kumar Karna, and Suraj Gautam. "Landslide Susceptibility Assessment Using Modified Frequency Ratio Model in Kaski District, Nepal." International Journal of Engineering and Management Research 11, no. 1 (February 27, 2021): 167–77. http://dx.doi.org/10.31033/ijemr.11.1.23.
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Landslides are the most common natural hazards in Nepal especially in the mountainous terrain. The existing topographical scenario, complex geological settings followed by the heavy rainfall in monsoon has contributed to a large number of landslide events in the Kaski district. In this study, landslide susceptibility was modeled with the consideration of twelve conditioning factors to landslides like slope, aspect, elevation, Curvature, geology, land-use, soil type, precipitation, road proximity, drainage proximity, and thrust proximity. A Google-earth-based landslide inventory map of 637 landslide locations was prepared using data from Disinventar, reports, and satellite image interpretation and was randomly subdivided into a training set (70%) with 446 Points and a test set with 191 points (30%). The relationship among the landslides and the conditioning factors were statistically evaluated through the use of Modified Frequency ratio analysis. The results from the analysis gave the highest Prediction rate (PR) of 6.77 for elevation followed by PR of 66.45 for geology and PR of 6.38 for the landcover. The analysis was then validated by calculating the Area Under a Curve (AUC) and the prediction rate was found to be 68.87%. The developed landslide susceptibility map is helpful for the locals and authorities in planning and applying different intervention measures in the Kaski District.
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Kaafarani, Rouba, Grace Abou-Jaoude, Joseph Wartman, and Miriam Tawk. "Landslide susceptibility mapping based on triggering factors using a multi-modal approach." MATEC Web of Conferences 281 (2019): 02002. http://dx.doi.org/10.1051/matecconf/201928102002.
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Landslide susceptibility mapping has been done using statistical and physically-based assessment techniques with limited focus on mode-specific models to identify failure modes and runout patterns. Because each failure mode has different consequences, it is essential to identify the failure mode associated with each slope inclination category, triggering factor, and geological setting. This paper presents a multimodal regionalscale assessment procedure for rainfall and earthquake-induced landslides, in the country of Lebanon, where landslide inventories are not available. Three failure modes are studied: debris flows, rock-slope failures, and coherent rotational slides. Areas prone to each mode of failure are identified based on geology and topography, then, using mode-specific models, their susceptibility to landslides is assessed. A runout assessment approach is then presented to identify the influence area of each predicted landslide and to obtain comprehensive susceptibility maps. Field assessment validated the proposed model which was in good agreement with actual slope failures across Lebanon. Therefore, the multimodal approach may be used to assess rainfall-induced landslide susceptibility, especially when landslide inventories are unavailable.
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Goyal, Deepesh, Varun Joshi, Neha Gupta, and Marina M. S. Cabral-Pinto. "Soil Quality Assessment in a Landslide Chronosequence of Indian Himalayan Region." Land 11, no. 10 (October 17, 2022): 1819. http://dx.doi.org/10.3390/land11101819.
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Landslides cause ecosystem degradation; they can significantly alter and deteriorate the soil quality. The analysis of deterioration in soil quality is critical as it provides baseline evidence for subsequent revegetation and management of forest. The effects of landslides on the natural environment (losses of soil resources), on the other hand, have received little consideration. Such information about the status of loss of soil resources in the landslide–disturbed areas of the Garhwal Himalayas is lacking. Therefore, the objective of the study is to assess the changes in soil quality restoration after the occurrence of landslides. A chronosequence of four landslide disturbed sites, L6–6–year–old, L16–16–year–old, L21–21–year–old and L26–26–year–old, was selected in the Alaknanda watershed of Uttarakhand. Seventy–six samples have been collected from the four landslide sites and a reference site (undisturbed site). The sites L6 and L16 are considered as recent landslide sites, whereas L21 and L26 are considered as old landslide sites. Entisols (Lithic–Udorthents) predominate in all the studied sites. The results have demonstrated that with the increasing age of landslides, the soil quality progressively improves with time, and the concentration of soil nutrients, viz., available phosphorus (AP), available potassium (AK) and mineralisable nitrogen (MN), in old landslide sites reaches to about 84%, 87% and 97%, respectively, of the reference site. Soil Quality Index (SQI) scores have been calculated using the Integrated Quality Index (IQI) equation. The disturbed sites L6, L16, L21 and L26 and the reference site have SQI scores of 0.136, 0.279, 0.447, 0.604 and 0.882, respectively. However, significant differences exist between the SQI of all the studied sites (p < 0.05, Tukey’s HSD), which implies that the concentration of soil organic carbon (SOC) and available nutrients was reduced due to the occurrence of landslides. The results also suggested that SOC, AP and clay fraction can be considered important evaluation indicators to assess soil quality and development.
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Lv, Zhi Qiang, Bin Sheng Wang, and Xiao Xue Zhang. "The PFC2DSimulation of the Slope Stability." Advanced Materials Research 1065-1069 (December 2014): 240–43. http://dx.doi.org/10.4028/www.scientific.net/amr.1065-1069.240.
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The main factors affecting landslide instability are landslides lithologic ,geological structure, topography, climate, hydrological conditions, earthquakes ,human and so on .The risk assessment of landslide is an effective method for analysis landslide stability ,but there has been little research for prediction of landslide in China .In this paper, particle flow procedures is used to analyze the landslide stability under natural conditions and forecast a possible landslide sliding surface ,and a scientific method will be provided for landslide prediction.
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Xu, Shenghua, Meng Zhang, Yu Ma, Jiping Liu, Yong Wang, Xinrui Ma, and Jie Chen. "Multiclassification Method of Landslide Risk Assessment in Consideration of Disaster Levels: A Case Study of Xianyang City, Shaanxi Province." ISPRS International Journal of Geo-Information 10, no. 10 (September 26, 2021): 646. http://dx.doi.org/10.3390/ijgi10100646.
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Geological disaster risk assessment can quantitatively assess the risk of disasters to hazard-bearing bodies. Visualizing the risk of geological disasters can provide scientific references for regional engineering construction, urban planning, and disaster prevention and mitigation. There are some problems in the current binary classification landslide risk assessment model, such as a single sample type, slow multiclass classification speed, large differences in the number of positive and negative samples, and large errors in classification results. This paper introduces multilevel landslide hazard scale samples, selects multiple types of samples according to the divided multilevel landslide hazard scale grade, and proposes a landslide hazard assessment model based on a multiclass support vector machine (SVM). Due to the objective limitations of the single weighting method, the combined weights are used to determine the vulnerability of the landslide hazard-bearing body, and the analytic hierarchy process (AHP) and entropy method are combined to construct a landslide vulnerability assessment model that considers subjective and objective weights. This paper takes landslide disasters in Xianyang City, Shaanxi Province, as the research object. Based on the landslide hazard assessment model and the landslide vulnerability assessment model, a landslide risk assessment experiment is carried out. It generates the landslide risk assessment zoning map and summarizes the risk characteristics of landslides in various towns. The experimental results verify the feasibility and effectiveness of the proposed model and provide important decision support for decision makers in Xianyang City.
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Jacobs, Liesbet, Olivier Dewitte, Jean Poesen, John Sekajugo, Adriano Nobile, Mauro Rossi, Wim Thiery, and Matthieu Kervyn. "Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains." Natural Hazards and Earth System Sciences 18, no. 1 (January 9, 2018): 105–24. http://dx.doi.org/10.5194/nhess-18-105-2018.
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Abstract. The inhabited zone of the Ugandan Rwenzori Mountains is affected by landslides, frequently causing loss of life, damage to infrastructure and loss of livelihood. This area of ca. 1230 km2 is characterized by contrasting geomorphologic, climatic and lithological patterns, resulting in different landslide types. In this study, the spatial pattern of landslide susceptibility is investigated based on an extensive field inventory constructed for five representative areas within the region (153 km2) and containing over 450 landslides. To achieve a reliable susceptibility assessment, the effects of (1) using different topographic data sources and spatial resolutions and (2) changing the scale of assessment by comparing local and regional susceptibility models on the susceptibility model performances are investigated using a pixel-based logistic regression approach. Topographic data are extracted from different digital elevation models (DEMs) based on radar interferometry (SRTM and TanDEM-X) and optical stereophotogrammetry (ASTER DEM). Susceptibility models using the radar-based DEMs tend to outperform the ones using the ASTER DEM. The model spatial resolution is varied between 10, 20, 30 and 90 m. The optimal resolution depends on the location of the investigated area within the region but the lowest model resolution (90 m) rarely yields the best model performances while the highest model resolution (10 m) never results in significant increases in performance compared to the 20 m resolution. Models built for the local case studies generally have similar or better performances than the regional model and better reflect site-specific controlling factors. At the regional level the effect of distinguishing landslide types between shallow and deep-seated landslides is investigated. The separation of landslide types allows us to improve model performances for the prediction of deep-seated landslides and to better understand factors influencing the occurrence of shallow landslides such as tangent curvature and total rainfall. Finally, the landslide susceptibility assessment is overlaid with a population density map in order to identify potential landslide risk hotspots, which could direct research and policy action towards reduced landslide risk in this under-researched, landslide-prone region.
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Tien Bui, Dieu, Himan Shahabi, Ataollah Shirzadi, Kamran Chapi, Mohsen Alizadeh, Wei Chen, Ayub Mohammadi, et al. "Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia." Remote Sensing 10, no. 10 (September 23, 2018): 1527. http://dx.doi.org/10.3390/rs10101527.
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Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.
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Mohd Salleh, M. R., N. I. Ishak, K. A. Razak, M. Z. Abd Rahman, M. A. Asmadi, Z. Ismail, and M. F. Abdul Khanan. "GEOSPATIAL APPROACH FOR LANDSLIDE ACTIVITY ASSESSMENT AND MAPPING BASED ON VEGETATION ANOMALIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W9 (October 30, 2018): 201–15. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w9-201-2018.
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<p><strong>Abstract.</strong> Remote sensing has been widely used for landslide inventory mapping and monitoring. Landslide activity is one of the important parameters for landslide inventory and it can be strongly related to vegetation anomalies. Previous studies have shown that remotely sensed data can be used to obtain detailed vegetation characteristics at various scales and condition. However, only few studies of utilizing vegetation characteristics anomalies as a bio-indicator for landslide activity in tropical area. This study introduces a method that utilizes vegetation anomalies extracted using remote sensing data as a bio-indicator for landslide activity analysis and mapping. A high-density airborne LiDAR, aerial photo and satellite imagery were captured over the landslide prone area along Mesilau River in Kundasang, Sabah. Remote sensing data used in characterizing vegetation into several classes of height, density, types and structure in a tectonically active region along with vegetation indices. About 13 vegetation anomalies were derived from remotely sensed data. There were about 14 scenarios were modeled by focusing in 2 landslide depth, 3 main landslide types with 3 landslide activities by using statistical approach. All scenarios show that more than 65% of the landslides are captured within 70% of the probability model indicating high model efficiency. The predictive model rate curve also shows that more than 45% of the independent landslides can be predicted within 30% of the probability model. This study provides a better understanding of remote sensing data in extracting and characterizing vegetation anomalies induced by hillslope geomorphology processes in a tectonically active region in Malaysia.</p>
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Muddarisna, Nurul, Eny Dyah Yuniwati, Heni Masruroh, and Aulia Rachman Oktaviansyah. "GEOMORPHOLOGY APPROACH AS LANDSLIDE ASSESSMENT IN THE GEDE CATCHMENT MALANG REGENCY." EPH - International Journal of Science And Engineering 5, no. 3 (September 27, 2019): 23–27. http://dx.doi.org/10.53555/eijse.v5i3.58.
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Gede catchment is the part of Bromo Volcano System. It is located in the Malang Regency, East Java Province. The wide of Gede Catchment is around 17 Km2. Landslide prone area mapping shows this area has high potential landslide around 52,9%. There were several landslides which had been occurred in this area. Based on the physical condition, this area is rather not appropriate for living. The aim of this research is to elaborate geomorphology approach as landslide assessment in the Gede Catchment. The method of this research is descriptive qualitative with survey technique. We assessed landslide based on the geomorphological approach and combined with the survey of landslide site. Based on the result shows that geomorphology is necessary factor for considering landslide assessment. Geomorphological approach is consist morphology, morphostructure, morphocronology, and morphoarrangement. The component of morphology is slope, component morphostructure and morphoarrangement is geomorphic process and for the component morphocronology is surface material and lithology. The four aspect of geomorphology become the keys for explain of landslide potential assessment. Geomorphology is the appropriate approach for landslide assessment in the Gede Catchment as the prone area for landslide. Otherwise, the potential of landslide will be difficult to determine if triggered by human activity or extreme rainfall.
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Guillard-Gonçalves, C., J. L. Zêzere, S. Pereira, and R. A. C. Garcia. "Assessment of physical vulnerability of buildings and analysis of landslide risk at the municipal scale: application to the Loures municipality, Portugal." Natural Hazards and Earth System Sciences 16, no. 2 (February 3, 2016): 311–31. http://dx.doi.org/10.5194/nhess-16-311-2016.
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Abstract. This study offers a semi-quantitative assessment of the physical vulnerability of buildings to landslides in a Portuguese municipality (Loures), as well as the quantitative landslide risk analysis computed as the product of the landslide hazard by the vulnerability and the economic value of the buildings. The hazard was assessed by combining the spatiotemporal probability and the frequency–magnitude relationship of the landslides. The physical vulnerability assessment was based on an inquiry of a pool of European landslide experts and a sub-pool of landslide experts who know the study area, and the answers' variability was assessed with standard deviation. The average vulnerability of the basic geographic entities was compared by changing the map unit and applying the vulnerability to all the buildings of a test site, the inventory of which was listed on the field. The economic value was calculated using an adaptation of the Portuguese Tax Services approach, and the risk was computed for different landslide magnitudes and different spatiotemporal probabilities. As a rule, the vulnerability values given by the sub-pool of experts who know the study area are higher than those given by the European experts, namely for the high-magnitude landslides. The obtained vulnerabilities vary from 0.2 to 1 as a function of the structural building types and the landslide magnitude, and are maximal for 10 and 20 m landslide depths. However, the highest risk was found for the landslides that are 3 m deep, because these landslides combine a relatively high frequency in the Loures municipality with a substantial potential damage.
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Marinos, V., G. Stoumpos, G. Papathanassiou, N. Grendas, D. Papouli, and C. Papazachos. "LANDSLIDE GEOHAZARD FOR PIPELINES OF NATURAL GAS TRANSPORT." Bulletin of the Geological Society of Greece 50, no. 2 (July 27, 2017): 845. http://dx.doi.org/10.12681/bgsg.11791.
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Landslides represent a significant hazard for pipelines because they can generate permanent ground displacement and tend to result in complete failure or significant leaks, major environmental impacts and long periods of service disruption. Hence, landslide-related incidents are regarded as a significant operational risk. The paper mainly focuses on the assessment of landslide hazard along or across a natural gas pipeline project and on the identification of these hazards, mostly in the field. Whether the “expected” landslide event reaches the Right of Way (RoW) and impacts the pipeline, is influenced by the nature and size of the expected landslide event, controlled by the site geology and geomorphology, the proximity of the existing landslide feature to the pipeline and the position of the pipeline relative to the landslide. Landslide hazard assessment is used to identify “hot spots” along the pipeline route where re-routing or risk reduction measures must be prioritised. When landslides that can threat the pipeline integrity cannot be avoided, more detailed site evaluation is required to support the design and construction of mitigation measures.
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Park, Hyuck-Jin, Kang-Min Kim, In-Tak Hwang, and Jung-Hyun Lee. "Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach." Sustainability 14, no. 5 (February 24, 2022): 2628. http://dx.doi.org/10.3390/su14052628.
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The accurate assessment of landslide hazards is important in order to reduce the casualties and damage caused by landslides. Landslide hazard assessment combines the evaluation of spatial and temporal probabilities. Although various statistical approaches have been used to estimate spatial probability, these methods only evaluate the statistical relationships between factors that have triggered landslides in the past rather than the slope failure process. Therefore, a physically based approach with probabilistic analysis was adopted here to estimate the spatial distribution of landslide probability. Meanwhile, few studies have addressed temporal probability because historical records of landslides are not available for most areas of the world. Therefore, an indirect approach based on rainfall frequency and using extreme value analysis and the Gumbel distribution is proposed and used in this study. In addition, to incorporate the nonstationary characteristics of rainfall data, an expanding window approach was used to evaluate changes in the mean annual maximum rainfall and the location and scale parameters of the Gumbel distribution. Using this approach, the temporal probabilities of future landslides were estimated and integrated with spatial probabilities to assess and map landslide hazards.
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Baek, M. H., and T. H. Kim. "A study on the use of planarity for quick identification of potential landslide hazard." Natural Hazards and Earth System Sciences 15, no. 5 (May 20, 2015): 997–1009. http://dx.doi.org/10.5194/nhess-15-997-2015.
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Abstract. In this study we focused on identifying a geomorphological feature that controls the location of landslides. The representation of the feature is based on a high-resolution digital elevation model derived from the airborne laser altimetry (LiDAR) and evaluated by the statistical analysis of axial orientation data. The main principle of this analysis is generating eigenvalues from axial orientation data and comparing them. The planarity, a ratio of eigenvalues, would tell the degree of irregularity on the ground surface based on their ratios. Results are compared to the recent landslide case in Korea in order to evaluate the feasibility of the proposed methodology in identifying the potential landslide hazard. The preliminary landslide hazard assessment based on the planarity analysis discriminates features between stable and unstable domain in the study area well, especially in the landslide initiation zones. Results also show it is beneficial to build the landslide hazard inventory mapping, especially where no information on historical records of landslides exists. By combining other physical procedures such as geotechnical monitoring, the landslide hazard assessment using geomorphological features promises a better understanding of landslides and their mechanisms and provides an enhanced methodology to evaluate their hazards and appropriate actions.
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Baek, M. H., and T. H. Kim. "A feasibility study on the influence of the geomorphological feature in identifying the potential landslide hazard." Natural Hazards and Earth System Sciences Discussions 2, no. 11 (November 20, 2014): 7119–47. http://dx.doi.org/10.5194/nhessd-2-7119-2014.
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Abstract. In this study we focused on identifying geomorphological features that control the location of landslides. The representation of these features is based on a high resolution DEM (Digital Elevation Model) derived from airborne laser altimetry (LiDAR) and evaluated by statistical analysis of axial orientation data. The main principle of this analysis is generating eigenvalues from axial orientation data and comparing them. The Planarity, a ratio of eigenvalues, would tell the degree of roughness on ground surface based on their ratios. Results are compared to the recent landslide case in Korea in order to evaluate the feasibility of the proposed methodology in identifying the potential landslide hazard. The preliminary landslide assessment based on the Planarity analysis well discriminates features between stable and unstable domain in the study area especially in the landslide initiation zones. Results also show it is beneficial to build the preliminary landslide hazard especially inventory mapping where none of information on historical records of landslides is existed. By combining other physical procedures such as geotechnical monitoring, the landslide hazard assessment using geomorphological features will promise a better understanding of landslides and their mechanisms, and provide an enhanced methodology to evaluate their hazards and appropriate actions.
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Khouz, Abdellah, Jorge Trindade, Sérgio C. Oliveira, Fatima El Bchari, Blaid Bougadir, Ricardo A. C. Garcia, and Mourad Jadoud. "Landslide susceptibility assessment in the rocky coast subsystem of Essaouira, Morocco." Natural Hazards and Earth System Sciences 22, no. 11 (November 24, 2022): 3793–814. http://dx.doi.org/10.5194/nhess-22-3793-2022.
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Abstract. In recent decades, multiple researchers have produced landslide susceptibility maps using different techniques and models, including the information value method, which is a statistical model that is widely applied to various coastal environments. This study aimed to evaluate susceptibility to landslides in the Essaouira coastal area using bivariate statistical methods. In this study, 588 distinct landslides were identified, inventoried, and mapped. Landslides are performed by means of observation and interpretation of different data sources, namely high-resolution satellite images, aerial photographs, topographic maps, and extensive field surveys. The rocky coastal system of Essaouira is located in the middle of the Atlantic coast of Morocco. The study area was split into 1534 cliff terrain units that were 50 m in width. For training and validation purposes, the landslide inventory was divided into two independent groups: 70 % for training and 30 % for validation. Twenty-two layers of landslide conditioning factors were prepared – namely, elevation, slope angle, slope aspect, plan curvature, profile curvature, cliff height, topographic wetness index, topographic position index, slope over area ratio, solar radiation, presence of faulting, lithological units, toe lithology, presence and type of cliff toe protection, layer tilt, rainfall, streams, land-use patterns, normalised difference vegetation index, lithological material grain size, and presence of springs. The statistical relationship between the conditioning factors and the different landslide types was calculated using the bivariate information value method in a pixel-based model and in the elementary terrain units-based model. Coastal landside susceptibility maps were validated using landslide training group partitions. The receiver operating characteristic curve and area under the curve were used to assess the accuracy and prediction capacity of the different coastal landslide susceptibility models. Two methodologies, considering a pixel-based approach and using coastal terrain units, were adopted to evaluate coastal landslide susceptibility. The results allowed for the classification of 38 % of the rocky coast subsystem as having high susceptibility to landslides, which were mostly located in the southern part of the Essaouira coastal area. These susceptibility maps will be useful for future planned development activities as well as for environmental protection.