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Mamun, Al, Hyun-Su Park, and Dong-Ho JANG. "공간예측모형에 기반한 산사태 취약성 지도 작성과 품질 평가." JOURNAL OF THE GEOMORPHOLOGICAL ASSOCIATION OF KOREA 26, no. 3 (September 30, 2019): 53–67. http://dx.doi.org/10.16968/jkga.26.3.53.
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Kritikos, Theodosios R. H., and Timohty R. H. Davies. "GIS-based Multi-Criteria Decision Analysis for landslide susceptibility mapping at northern Evia, Greece." Zeitschrift der Deutschen Gesellschaft für Geowissenschaften 162, no. 4 (December 1, 2011): 421–34. http://dx.doi.org/10.1127/1860-1804/2011/0162-0421.
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Chawla, Amit, Sowmiya Chawla, Srinivas Pasupuleti, A. C. S. Rao, Kripamoy Sarkar, and Rajesh Dwivedi. "Landslide Susceptibility Mapping in Darjeeling Himalayas, India." Advances in Civil Engineering 2018 (September 16, 2018): 1–17. http://dx.doi.org/10.1155/2018/6416492.
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Landslide susceptibility map aids decision makers and planners for the prevention and mitigation of landslide hazard. This study presents a methodology for the generation of landslide susceptibility mapping using remote sensing data and Geographic Information System technique for the part of the Darjeeling district, Eastern Himalaya, in India. Topographic, earthquake, and remote sensing data and published geology, soil, and rainfall maps were collected and processed using Geographic Information System. Landslide influencing factors in the study area are drainage, lineament, slope, rainfall, earthquake, lithology, land use/land cover, fault, valley, soil, relief, and aspect. These factors were evaluated for the generation of thematic data layers. Numerical weight and rating for each factor was assigned using the overlay analysis method for the generation of landslide susceptibility map in the Geographic Information System environment. The resulting landslide susceptibility zonation map demarcated the study area into four different susceptibility classes: very high, high, moderate, and low. Particle Swarm Optimization-Support Vector Machine technique was used for the prediction and classification of landslide susceptibility classes, and Genetic Programming method was used to generate models and to predict landslide susceptibility classes in conjunction with Geographic Information System output, respectively. Genetic Programming and Particle Swarm Optimization-Support Vector Machine have performed well with respect to overall prediction accuracy and validated the landslide susceptibility model generated in the Geographic Information System environment. The efficiency of the landslide susceptibility zonation map was also confirmed by correlating the landslide frequency between different susceptible classes.
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Lin, Zian, Qiuguang Chen, Weiping Lu, Yuanfa Ji, Weibin Liang, and Xiyan Sun. "Landslide Susceptibility Mapping Based on Information-GRUResNet Model in the Changzhou Town, China." Forests 14, no. 3 (March 2, 2023): 499. http://dx.doi.org/10.3390/f14030499.
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Landslide susceptibility mapping is the basis of regional landslide risk assessment and prevention. In recent years, deep learning models have been applied in landslide susceptibility mapping, but some problems remain, such as gradient disappearance, explosion, and degradation. Additionally, the potential nonlinear temporal and spatial characteristics between landslides and environmental factors may not be captured, and nonlandslide points may be randomly selected in the susceptibility mapping process. To overcome these shortcomings, in this paper, an information-gate recurrent unit residual network (Information-GRUResNet) model is proposed to produce a landslide susceptibility map by combining existing landslide records and environmental factor data. The model uses the information theory method to produce the initial landslide susceptibility map. Then, representative grid units and landslide points are selected as input variables of the GRUResNet model, from which nonlinear temporal and spatial characteristics are extracted to produce a landslide susceptibility map. Changzhou town in Wuzhou, China, is selected as a case study, and it is verified that the Information-GRUResNet model can accurately produce a landslide susceptibility map for the selected area. Finally, the Information-GRUResNet model is compared with GRU, RF, and LR models. The experimental results show that the Information-GRUResNet model is more accurate than the other three models.
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Bathrellos, G. D., D. P. Kalivas, and H. D. Skilodimou. "GIS-based landslide susceptibility mapping models applied to natural and urban planning in Trikala, Central Greece." Estudios Geológicos 65, no. 1 (December 9, 2008): 49–65. http://dx.doi.org/10.3989/egeol.08642.036.
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Sekarlangit, Nadia, Teuku Faisal Fathani, and Wahyu Wilopo. "Landslide Susceptibility Mapping of Menoreh Mountain Using Logistic Regression." Journal of Applied Geology 7, no. 1 (June 28, 2022): 51. http://dx.doi.org/10.22146/jag.72067.
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Menoreh mountain is one of the priority areas developed for tourism and to support sustainable development, it must pay attention to disaster aspects, one of which is landslides. The map published by Center for Volcanology and Geological Hazard Mitigation of Indonesia (PVMBG) has a regional scale, so it is necessary to have a more detailed landslide susceptibility map in the Menoreh Mountains. Identification and evaluation of the landslide conditioning factor were done using logistic regression so that the zonation of the probability of landslide susceptibility can be made. The data was used from field observation conducted at 372 locations including 129 locations where landslides occurred and from a local disaster management agency (BPBD) of 200 landslide locations. Significant landslide conditioning factors include slope, lithology, distance to lineaments, distance to river, and distance to road. The research area is divided into three susceptibility zones classified into low landslide susceptibility zone (0-0.33) covering 39.82%, moderate landslide susceptibility zone (0.34-0.66) covering 25.86%, and high landslide susceptibility zone (0.67-1.00) covering 34.31% of the whole area. Analysis using the logistic regression method has a model prediction accuracy rate of 90.5%, which means that it can predict landslide occurrence in the Menoreh Mountains accurately.
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Bornaetxea, Txomin, Mauro Rossi, Ivan Marchesini, and Massimiliano Alvioli. "Effective surveyed area and its role in statistical landslide susceptibility assessments." Natural Hazards and Earth System Sciences 18, no. 9 (September 14, 2018): 2455–69. http://dx.doi.org/10.5194/nhess-18-2455-2018.
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Abstract. Geomorphological field mapping is a conventional method used to prepare landslide inventories. The approach is typically hampered by the accessibility and visibility, during field campaigns for landslide mapping, of the different portions of the study area. Statistical significance of landslide susceptibility maps can be significantly reduced if the classification algorithm is trained in unsurveyed regions of the study area, for which landslide absence is typically assumed, while ignorance about landslide presence should actually be acknowledged. We compare different landslide susceptibility zonations obtained by training the classification model either in the entire study area or in the only portion of the area that was actually surveyed, which we name effective surveyed area. The latter was delineated by an automatic procedure specifically devised for the purpose, which uses information gathered during surveys, along with landslide locations. The method was tested in Gipuzkoa Province (Basque Country), north of the Iberian Peninsula, where digital thematic maps were available and a landslide survey was performed. We prepared the landslide susceptibility maps and the associated uncertainty within a logistic regression model, using both slope units and regular grid cells as the reference mapping unit. Results indicate that the use of effective surveyed area for landslide susceptibility zonation is a valid approach that minimises the limitations stemming from unsurveyed regions at landslide mapping time. Use of slope units as mapping units, instead of grid cells, mitigates the uncertainties introduced by training the automatic classifier within the entire study area. Our method pertains to data preparation and, as such, the relevance of our conclusions is not limited to the logistic regression but are valid for virtually all the existing multivariate landslide susceptibility models.
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Habumugisha, Jules Maurice, Ningsheng Chen, Mahfuzur Rahman, Md Monirul Islam, Hilal Ahmad, Ahmed Elbeltagi, Gitika Sharma, Sharmina Naznin Liza, and Ashraf Dewan. "Landslide Susceptibility Mapping with Deep Learning Algorithms." Sustainability 14, no. 3 (February 2, 2022): 1734. http://dx.doi.org/10.3390/su14031734.
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Among natural hazards, landslides are devastating in China. However, little is known regarding potential landslide-prone areas in Maoxian County. The goal of this study was to apply four deep learning algorithms, the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) networks, and recurrent neural network (RNN) in evaluating the possibility of landslides throughout Maoxian County, Sichuan, China. A total of 1290 landslide records was developed using historical records, field observations, and remote sensing techniques. The landslide susceptibility maps showed that most susceptible areas were along the Minjiang River and in some parts of the southeastern portion of the study area. Slope, rainfall, and distance to faults were the most influential factors affecting landslide occurrence. Results revealed that proportion of landslide susceptible areas in Maoxian County was as follows: identified landslides (13.65–23.71%) and non-landslides (76.29–86.35%). The resultant maps were tested against known landslide locations using the area under the curve (AUC). This study indicated that the DNN algorithm performed better than LSTM, CNN, and RNN in identifying landslides in Maoxian County, with AUC values (for prediction accuracy) of 87.30%, 86.50%, 85.60%, and 82.90%, respectively. The results of this study are useful for future landslide risk reduction along with devising sustainable land use planning in the study area.
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Thanh, Dang Quang, Duy Huu Nguyen, Indra Prakash, Abolfazl Jaafari, Viet Tien Nguyen, Tran Van Phong, and Binh Thai Pham. "GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam." VIETNAM JOURNAL OF EARTH SCIENCES 42, no. 1 (January 15, 2020): 55–66. http://dx.doi.org/10.15625/0866-7187/42/1/14758.
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Landslide susceptibility mapping of the city of Da Lat, which is located in the landslide prone area of Lam Dong province of Central Vietnam region, was carried out using GIS based frequency ratio (FR) method. There are number of methods available but FR method is simple and widely used method for landslide susceptibility mapping. In the present study, eight topographical and geo-environmental landslide-conditioning factors were used including slope, elevation, land use, weathering crust, soil, lithology, distance to geology features, and stream density in conjunction with 70 past landslide locations. The results show that 6.27% of the area is in the very low susceptibility area, 21.03% in the low susceptibility area, 27.09% in the moderate susceptibility area and 27.41% of the area is in the high susceptibility zone and 18.21% in the very high susceptibility zone. The landslide susceptibility map produced in this study helps to assist decision makers in proper land use management and planning.
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Yu, Chenglong, and Jianping Chen. "Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model." Symmetry 12, no. 11 (November 9, 2020): 1848. http://dx.doi.org/10.3390/sym12111848.
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Landslides are one of the most extensive geological disasters in the world. The objective of this study was to assess the performances of different landslide susceptibility models information content method (ICM), analytical hierarchy process (AHP), and random forest (RF) model) and mapping unit (slope unit and grid unit) for landslide susceptibility mapping in the Helong city, Jilin province, northeastern China. First, a total of 159 landslides were mapped in the study area based on a geological hazard survey (1:50,000) of Helong city. Then, the slope units of the study area were divided by using the curvature watershed method. Next, eight influencing factors, namely, lithology, slope angle, slope aspect, rainfall, land use, seismic intensity, distance to river, and distance to fault, were selected to map the landslide susceptibility based on geological data, field survey, and landslide information. Afterward, landslide susceptibility modeling of landslide inventory data is performed for extracting and learning the symmetry latent in data patterns and relationships by three landslide susceptibility models and utilizing it to predict landslide susceptibility. Finally, the receiver operating characteristic (ROC) curve was used to compare the landslide susceptibility models. In addition, results based on grid units were calculated for comparison. The AUC (the area under the curve) result for ICM, AHP, and RF model was 87.1%, 80.5%, and 94.6% for slope units, and 83.4%, 70.9%, and 91.3% for grid units, respectively. Based on the overall assessments, the SU-RF model was the most suitable model for landslide susceptibility mapping. Consequently, these methods can be very useful for landslide hazard mitigation strategies.
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Ali, M. Z., H. J. Chu, S. Ullah, M. Shafique, and A. Ali. "UTILIZATION OF FINE RESOLUTION SATELLITE DATA FOR LANDSLIDE SUSCEPTIBILITY MODELLING: A CASE STUDY OF KASHMIR EARTHQUAKE INDUCED LANDSLIDES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 20, 2019): 25–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-25-2019.
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<p><strong>Abstract.</strong> The 2005 Kashmir earthquake has triggered thousands of landslides which devastated most of the livelihood and other infrastructure in the area. Landslide inventory and subsequently landslide susceptibility mapping is one of the main prerequisite for taking mitigation measure against landslide effects. This study has focused on developing most updated and realistic landslide inventory and Susceptibility mapping. The high resolution data of Worldveiw-2 having spatial resolution of 0.4 m is used for landslide inventory. Support Vector Machine (SVM) classifier was used for landslide inventory developing. Total 51460 number of landslides were classified using semi-automatic technique with covering area of 265 Km<sup>2</sup>, smallest landslide mapped is covering area of 2.01 m<sup>2</sup> and the maximum covered area of single landslide is 3.01 Km<sup>2</sup>. Nine influential causative factors are used for landslide susceptibility mapping. Those causative factors include slope, aspect, profile curvature, elevation, distance from fault lines, distance from streams and geology. Logistic regression model was used for the Landslides susceptibility modelling. From model the highest coefficient was assigned to geology which shows that the geology has higher influence in the area. For landslide susceptibility mapping the 70 % of the data was used and 30% is used for the validation of the model. The prediction accuracy of the model in this study is 92 % using validation data. This landslide susceptibility map can be used for land use planning and also for the mitigation measure during any disaster.</p>
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Li, Bohao, Kai Liu, Ming Wang, Qian He, Ziyu Jiang, Weihua Zhu, and Ningning Qiao. "Global Dynamic Rainfall-Induced Landslide Susceptibility Mapping Using Machine Learning." Remote Sensing 14, no. 22 (November 16, 2022): 5795. http://dx.doi.org/10.3390/rs14225795.
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Precipitation is the main factor that triggers landslides. Rainfall-induced landslide susceptibility mapping (LSM) is crucial for disaster prevention and disaster losses mitigation, though most studies are temporally ambiguous and on a regional scale. To better reveal landslide mechanisms and provide more accurate landslide susceptibility maps for landslide risk assessment and hazard prediction, developing a global dynamic LSM model is essential. In this study, we used Google Earth Engine (GEE) as the main data platform and applied three tree-based ensemble machine learning algorithms to construct global, dynamic rainfall-induced LSM models based on dynamic and static landslide influencing factors. The dynamic perspective is used in LSM: dynamic changes in landslide susceptibility can be identified on a daily scale. We note that Random Forest algorithm offers robust performance for accurate LSM (AUC = 0.975) and although the classification accuracy of LightGBM is the highest (AUC = 0.977), the results do not meet the sufficient conditions of a landslide susceptibility map. Combined with quantitative precipitation products, the proposed model can be used for the release of historical and predictive global dynamic landslide susceptibility information.
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Wang, Guirong, Xinxiang Lei, Wei Chen, Himan Shahabi, and Ataollah Shirzadi. "Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping." Symmetry 12, no. 3 (February 25, 2020): 325. http://dx.doi.org/10.3390/sym12030325.
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In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance.
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Khaidem, Sukhajit, and Kanwarpreet Singh. "Landslide Susceptibility Mapping along Manipur-Assam NH-37." IOP Conference Series: Earth and Environmental Science 889, no. 1 (November 1, 2021): 012002. http://dx.doi.org/10.1088/1755-1315/889/1/012002.
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Abstract Landslides are a natural hazard in steep places that occur regularly and cause significant damage. To avoid and minimise hazards, comprehensive landslide remediation and control, landslide assessment, and hazard zonation are required. Various methods are established based on different assessment methodologies, which are essentially split into qualitative and quantitative approaches. GIS-based landslide susceptibility mapping was carried out along the National Highway 37, which connects Assam and Manipur and is a vital lifeline for the state, to identify and demarcate possible failure zones. A field visit was used to create a landslide inventory map along the road network. Google Earth and LANDSAT satellite imagery To perform landslide susceptibility zonation, thematic layers of several landslide causative elements were constructed in the study region. The study region has been divided into five endangered zones i.e. (“very low, low, moderate, high, and extremely high”). The landslide susceptibility zonation map was validated using the AUC and landslide density methods. The final map will be helpful to a variety of stakeholders, including town planners, engineers, geotechnical engineers, and geologists, for development and construction in the study region.
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Yang, Xin, Rui Liu, Luyao Li, Mei Yang, and Yuantao Yang. "Landslide susceptibility mapping using machine learning for Wenchuan County, Sichuan province, China." E3S Web of Conferences 198 (2020): 03023. http://dx.doi.org/10.1051/e3sconf/202019803023.
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Landslide susceptibility mapping is a method used to assess the probability and spatial distribution of landslide occurrences. Machine learning methods have been widely used in landslide susceptibility in recent years. In this paper, six popular machine learning algorithms namely logistic regression, multi-layer perceptron, random forests, support vector machine, Adaboost, and gradient boosted decision tree were leveraged to construct landslide susceptibility models with a total of 1365 landslide points and 14 predisposing factors. Subsequently, the landslide susceptibility maps (LSM) were generated by the trained models. LSM shows the main landslide zone is concentrated in the southeastern area of Wenchuan County. The result of ROC curve analysis shows that all models fitted the training datasets and achieved satisfactory results on validation datasets. The results of this paper reveal that machine learning methods are feasible to build robust landslide susceptibility models.
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Adnan, Mohammed Sarfaraz Gani, Md Salman Rahman, Nahian Ahmed, Bayes Ahmed, Md Fazleh Rabbi, and Rashedur M. Rahman. "Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping." Remote Sensing 12, no. 20 (October 14, 2020): 3347. http://dx.doi.org/10.3390/rs12203347.
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Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions.
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Pawluszek-Filipiak, Kamila, Natalia Oreńczak, and Marta Pasternak. "Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping." Applied Sciences 10, no. 18 (September 11, 2020): 6335. http://dx.doi.org/10.3390/app10186335.
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To mitigate the negative effects of landslide occurrence, there is a need for effective landslide susceptibility mapping (LSM). The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas where landslide inventories are not generated due to financial or reachability constraints. Considering this led to the following research question: can we model landslide susceptibility in an area for which landslide inventory is not available but where such is available for surrounding areas? To answer this question, we performed cross-modeling by using various strategies for landslide susceptibility. Namely, landslide susceptibility was cross-modeled by using two adjacent regions (“Łososina” and “Gródek”) separated by the Rożnów Lake and Dunajec River. Thus, 46% and 54% of the total detected landslides were used for the LSM in “Łososina” and “Gródek” model, respectively. Various topographical, geological, hydrological and environmental landslide-conditioning factors (LCFs) were created. These LCFs were generated on the basis of the Digital Elevation Model (DEM), Sentinel-2A data, a digitized geological and soil suitability map, precipitation, the road network and the Różnów lake shapefile. For LSM, we applied the Frequency Ratio (FR) and Landslide Susceptibility Index (LSI) methods. Five zones showing various landslide susceptibilities were generated via Natural Jenks. The Seed Cell Area Index (SCAI) and Relative Landslide Density Index were used for model validation. Even when the SCAI indicated extremely high values for “very low” susceptibility classes and very small values for “very high” susceptibility classes in the training and validation areas, the accuracy of the LSM in the validation areas was significantly lower. In the “Łososina” model, 90% and 57% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. In the “Gródek” model, 86% and 46% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. Moreover, the comparison between these two models was performed. Discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2–3 susceptibility zone difference). However, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be useful. Therefore, the low reliability of such a map in areas of critical geological structures should be borne in mind.
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Harmouzi, Hasnaa, Romy Schlögel, Marta Jurchescu, and Hans-Balder Havenith. "Landslide Susceptibility Mapping in the Vrancea-Buzău Seismic Region, Southeast Romania." Geosciences 11, no. 12 (December 3, 2021): 495. http://dx.doi.org/10.3390/geosciences11120495.
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This study presents the results of a landslide susceptibility analysis applied to the Vrancea-Buzău seismogenic region in the Carpathian Mountains, Romania. The target area is affected by a large diversity of landslide processes. Slopes are made-up of various types of rocks, climatic conditions can be classified as wet, and the area is a seismically active one. All this contributes to the observed high landslide hazard. The paper analyses the spatial component of the landslide hazard affecting the target area, the regional landslide susceptibility. First, an existing landslide inventory was completed to cover a wider area for the landslide susceptibility analysis. Second, two types of methods are applied, a purely statistical technique, based on correlations between landslide occurrence and local conditions, as well as the simplified spatial process-based Newmark Displacement analysis. Landslide susceptibility maps have been produced by applying both methods, the second one also allowing us to simulate different scenarios, based on various soil saturation rates and seismic inputs. Furthermore, landslide susceptibility was computed both for the landslide source and runout zones—the first providing information about areas where landslides are preferentially triggered and the second indicating where landslides preferentially move along the slope and accumulate. The analysis showed that any of the different methods applied produces reliable maps of landslide susceptibility. However, uncertainties were also outlined as validation is insufficient, especially in the northern area, where only a few landslides could be mapped due to the intense vegetation cover.
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Dias, Helen Cristina, Daniel Hölbling, and Carlos Henrique Grohmann. "Landslide Susceptibility Mapping in Brazil: A Review." Geosciences 11, no. 10 (October 15, 2021): 425. http://dx.doi.org/10.3390/geosciences11100425.
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Landslide susceptibility studies are a common type of landslide assessment. Landslides are one of the most frequent hazards in Brazil, resulting in significant economic and social losses (e.g., deaths, injuries, and property destruction). This paper presents a literature review of susceptibility mapping studies in Brazil and analyzes the methods and input data commonly used. The publications used in this analysis were extracted from the Web of Science platform. We considered the following aspects: location of study areas, year and where the study was published, methods, thematic variables, source of the landslide inventory, and validation methods. The susceptibility studies are concentrated in Brazil’s south and southeast region, with the number of publications increasing since 2015. The methods commonly used are slope stability and statistical models. Validation was performed based on receiver operating characteristic (ROC) curves and area under the curve (AUC). Even though landslide inventories constitute the most critical input data for susceptibility mapping, the criteria used for the creation of landslide inventories are not evident in most cases. The included studies apply various validation techniques, but evaluations with potential users and information on the practical applicability of the results are largely missing.
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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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Pasang, Sangey, and Petr Kubíček. "Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan." Geosciences 10, no. 11 (October 29, 2020): 430. http://dx.doi.org/10.3390/geosciences10110430.
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In areas prone to frequent landslides, the use of landslide susceptibility maps can greatly aid in the decision-making process of the socio-economic development plans of the area. Landslide susceptibility maps are generally developed using statistical methods and geographic information systems. In the present study, landslide susceptibility along road corridors was considered, since the anthropogenic impacts along a road in a mountainous country remain uniform and are mainly due to road construction. Therefore, we generated landslide susceptibility maps along 80.9 km of the Asian Highway (AH48) in Bhutan using the information value, weight of evidence, and logistic regression methods. These methods have been used independently by some researchers to produce landslide susceptibility maps, but no comparative analysis of these methods with a focus on road corridors is available. The factors contributing to landslides considered in the study are land cover, lithology, elevation, proximity to roads, drainage, and fault lines, aspect, and slope angle. The validation of the method performance was carried out by using the area under the curve of the receiver operating characteristic on training and control samples. The area under the curve values of the control samples were 0.883, 0.882, and 0.88 for the information value, weight of evidence, and logistic regression models, respectively, which indicates that all models were capable of producing reliable landslide susceptibility maps. In addition, when overlaid on the generated landslide susceptibility maps, 89.3%, 85.6%, and 72.2% of the control landslide samples were found to be in higher-susceptibility areas for the information value, weight of evidence, and logistic regression methods, respectively. From these findings, we conclude that the information value method has a better predictive performance than the other methods used in the present study. The landslide susceptibility maps produced in the study could be useful to road engineers in planning landslide prevention and mitigation works along the highway.
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Liu, Yimo, Wanchang Zhang, Zhijie Zhang, Qiang Xu, and Weile Li. "Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake." Remote Sensing 13, no. 6 (March 18, 2021): 1157. http://dx.doi.org/10.3390/rs13061157.
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Landslide susceptibility mapping is an effective approach for landslide risk prevention and assessments. The occurrence of slope instability is highly correlated with intrinsic variables that contribute to the occurrence of landslides, such as geology, geomorphology, climate, hydrology, etc. However, feature selection of those conditioning factors to constitute datasets with optimal predictive capability effectively and accurately is still an open question. The present study aims to examine further the integration of the selected landslide conditioning factors with Q-statistic in Geo-detector for determining stratification and selection of landslide conditioning factors in landslide risk analysis as to ultimately optimize landslide susceptibility model prediction. The location chosen for the study was Atsuma Town, which suffered from landslides following the Eastern Iburi Earthquake in 2018 in Hokkaido, Japan. A total of 13 conditioning factors were obtained from different sources belonging to six categories: geology, geomorphology, seismology, hydrology, land cover/use and human activity; these were selected to generate the datasets for landslide susceptibility mapping. The original datasets of landslide conditioning factors were analyzed with Q-statistic in Geo-detector to examine their explanatory powers regarding the occurrence of landslides. A Random Forest (RF) model was adopted for landslide susceptibility mapping. Subsequently, four subsets, including the Manually delineated landslide Points with 9 features Dataset (MPD9), the Randomly delineated landslide Points with 9 features Dataset (RPD9), the Manually delineated landslide Points with 13 features Dataset (MPD13), and the Randomly delineated landslide Points with 13 features Dataset (RPD13), were selected by an analysis of Q-statistic for training and validating the Geo-detector-RF- integrated model. Overall, using dataset MPD9, the Geo-detector-RF-integrated model yielded the highest prediction accuracy (89.90%), followed by using dataset MPD13 (89.53%), dataset RPD13 (88.63%) and dataset RPD9 (87.07%), which implied that optimized conditioning factors can effectively improve the prediction accuracy of landslide susceptibility mapping.
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Ji, Junjie, Yongzhang Zhou, Qiuming Cheng, Shoujun Jiang, and Shiting Liu. "Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization." Land 12, no. 6 (May 25, 2023): 1125. http://dx.doi.org/10.3390/land12061125.
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Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative samples used for machine learning. Recurrent neural network (RNN) has a memory function, so when using an RNN for landslide susceptibility mapping purposes, the input order of the landslide-influencing factors affects the resulting quality of the model. The information value analysis calculates the landslide-influencing factors, determines the input order of data based on the importance of any specific factor in determining the landslide susceptibility, and improves the prediction potential of recurrent neural networks. The simple recurrent unit (SRU), a newly proposed variant of the recurrent neural network, is characterized by possessing a faster processing speed and currently has less application history in landslide susceptibility mapping. This study used recurrent neural networks optimized by information value analysis for landslide susceptibility mapping in Xinhui District, Jiangmen City, Guangdong Province, China. Four models were constructed: the RNN model with optimized negative sample selection, the SRU model with optimized negative sample selection, the RNN model, and the SRU model. The results show that the RNN model with optimized negative sample selection has the best performance in terms of AUC value (0.9280), followed by the SRU model with optimized negative sample selection (0.9057), the RNN model (0.7277), and the SRU model (0.6355). In addition, several objective measures of accuracy (0.8598), recall (0.8302), F1 score (0.8544), Matthews correlation coefficient (0.7206), and the receiver operating characteristic also show that the RNN model performs the best. Therefore, the information value analysis can be used to optimize negative sample selection in landslide sensitivity mapping in order to improve the model’s performance; second, SRU is a weaker method than RNN in terms of model performance.
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Alkhasawne, Mutasem Sh, Umi Kalthum Bt Ngah, Tay Lea Tien, and Nor Ashidi Bin Mat Isa. "Landslide Susceptibility Hazard Mapping Techniques Review." Journal of Applied Sciences 12, no. 9 (April 15, 2012): 802–8. http://dx.doi.org/10.3923/jas.2012.802.808.
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Mokarram, Marzieh, and Abdol Rassoul Zarei. "Landslide Susceptibility Mapping Using Fuzzy-AHP." Geotechnical and Geological Engineering 36, no. 6 (May 28, 2018): 3931–43. http://dx.doi.org/10.1007/s10706-018-0583-y.
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Hearn, G. J., and A. B. Hart. "Landslide susceptibility mapping: a practitioner’s view." Bulletin of Engineering Geology and the Environment 78, no. 8 (May 21, 2019): 5811–26. http://dx.doi.org/10.1007/s10064-019-01506-1.
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Sugiarti, K., and S. Sukristiyanti. "TRIGRS Application for landslide susceptibility mapping." IOP Conference Series: Earth and Environmental Science 118 (February 2018): 012040. http://dx.doi.org/10.1088/1755-1315/118/1/012040.
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Lee, S., and D. G. Evangelista. "Earthquake-induced landslide-susceptibility mapping using an artificial neural network." Natural Hazards and Earth System Sciences 6, no. 5 (July 26, 2006): 687–95. http://dx.doi.org/10.5194/nhess-6-687-2006.
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Abstract. The purpose of this study was to apply and verify landslide-susceptibility analysis techniques using an artificial neural network and a Geographic Information System (GIS) applied to Baguio City, Philippines. The 16 July 1990 earthquake-induced landslides were studied. Landslide locations were identified from interpretation of aerial photographs and field survey, and a spatial database was constructed from topographic maps, geology, land cover and terrain mapping units. Factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from faults were derived from the geology database. Land cover was identified from the topographic database. Terrain map units were interpreted from aerial photographs. These factors were used with an artificial neural network to analyze landslide susceptibility. Each factor weight was determined by a back-propagation exercise. Landslide-susceptibility indices were calculated using the back-propagation weights, and susceptibility maps were constructed from GIS data. The susceptibility map was compared with known landslide locations and verified. The demonstrated prediction accuracy was 93.20%.
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Efendi, Didik, Entin Hidayah, and Akhmad Hasanuddin. "Mapping of Landslide Susceptible Zones by Using Frequency Ratios at Bluncong Subwatershed, Bondowoso Regency." UKaRsT 5, no. 1 (April 3, 2021): 126. http://dx.doi.org/10.30737/ukarst.v5i1.1455.
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Landslides are the disasters that frequently happen in Bluncong sub-watershed. These incidents have caused damage and malfunction of road infrastructure, bridges, and irrigation buildings. Therefore, it is important to anticipate landslides through mapping of landslide-susceptibility areas The objective of this study is to map landslide susceptibility at Bluncong sub watershed, Bondowoso, by using Geographical Information System and remote sensing. The landslide susceptibility analysis and mapping are conducted based on landslide occurrences with the Frequency Ratio approach. The landslide sites are identified from field survey data interpretation. Digital Elevation Model maps, geological data, land uses and rivers data, and Landsat 8 images are collected, processed, and then built into the GIS platform's spatial database. The selected factors that cause landslide occurrences are land use, distance to river, aspect, slope, elevation, curvature, and the vegetation index (NDVI). The results show that the accuracy of the map is acceptable. The frequency ratio model gained the area under curve (AUC) value of 0.79. It is found that 9.08% of the area has very high landslide susceptibility. Local governments can use this study's mapping results to minimize the risk at landslidesusceptible zones
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Mora, O. E., M. G. Lenzano, C. K. Toth, and D. A. Grejner-Brzezinska. "Analyzing the Effects of Spatial Resolution for Small Landslide Susceptibility and Hazard Mapping." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 293–300. http://dx.doi.org/10.5194/isprsarchives-xl-1-293-2014.
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Spatial resolution plays an important role in remote sensing technology as it defines the smallest scale at which surface features may be extracted, identified, and mapped. Remote sensing technology has become a vital component in recent developments for landslide susceptibility mapping. The spatial resolution is essential, especially when landslides are small and the dimensions of slope failures vary. If the spatial resolution is relevant to the surface features found in the landslide morphology, it will help improve the extraction, identification and mapping of landslide surface features. Although, the spatial resolution is a well-known issue, few studies have demonstrated the potential effects it may have on small landslide susceptibility mapping. For these reasons, an evaluation to assess the impact of spatial resolution was performed using data acquired along a transportation corridor in Zanesville, Ohio. Using a landslide susceptibility mapping algorithm, landslide surface features were extracted and identified on a cell-by-cell basis from Digital Elevation Models (DEM) generated at 50, 100, 200 and 400 cm spatial resolution. The performance of the landslide surface feature extraction algorithm was then evaluated using an inventory map and a confusion matrix to assess the effects of spatial resolution. In addition to assessing the performance of the algorithm, we statistically analyzed the surface features and their relevant patterns. The results from this evaluation reveal patterns caused by the varying spatial resolution. From this study we can conclude that the spatial resolution has an effect on the accuracy and surface features extracted for small landslide susceptibility mapping, as the performance is dependent on the scale of the landslide morphology.
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Zhao, Shuai, and Zhou Zhao. "A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units." Mathematical Problems in Engineering 2021 (January 15, 2021): 1–15. http://dx.doi.org/10.1155/2021/8854606.
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The main purpose of this study aims to apply and compare the rationality of landslide susceptibility maps using support vector machine (SVM) and particle swarm optimization coupled with support vector machine (PSO-SVM) models in Lueyang County, China, enhance the connection with the natural terrain, and analyze the application of grid units and slope units. A total of 186 landslide locations were identified by earlier reports and field surveys. The landslide inventory was randomly divided into two parts: 70% for training dataset and 30% for validation dataset. Based on the multisource data and geological environment, 16 landslide conditioning factors were selected, including control factors and triggering factors (i.e., altitude, slope angle, slope aspect, plan curvature, profile curvature, SPI, TPI, TRI, lithology, distance to faults, TWI, distance to rivers, NDVI, distance to roads, land use, and rainfall). The susceptibility between each conditioning factor and landslide was deduced using a certainty factor model. Subsequently, combined with grid units and slope units, the landslide susceptibility models were carried out by using SVM and PSO-SVM methods. The precision capability of the landslide susceptibility mapping produced by different models and units was verified through a receiver operating characteristic (ROC) curve. The results showed that the PSO-SVM model based on slope units had the best performance in landslide susceptibility mapping, and the area under the curve (AUC) values of training and validation datasets are 0.945 and 0.9245, respectively. Hence, the machine learning algorithm coupled with slope units can be considered a reliable and effective technique in landslide susceptibility mapping.
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Jamil, Rohazaini Muhammad, Farhah Nabilah Fadzil, Siti Aisyah Nawawi, Amal Najihah Muhamad Nor, Noorzamzarina Sulaiman, Nursufiah Sulaiman, and Norfadhilah Ibrahim. "Landslide Susceptibility Mapping Using Geographic Information System (GIS) in Kuala Balah, Jeli, Kelantan." IOP Conference Series: Earth and Environmental Science 1102, no. 1 (November 1, 2022): 012048. http://dx.doi.org/10.1088/1755-1315/1102/1/012048.
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A landslide is a natural occurrence that frequently results in the loss of property and life of those who live in the surrounding area. This study aims to produce a landslide susceptibility map by using Geographical Information System (GIS) approach in Kuala Balah, Jeli, Kelantan. Additionally, the geomorphology of this study area is hilly and mountainous, with several steep slopes that exposes citizens to the risk of landslides. In order to produce the landslide susceptibility map, six (6) parameters were applied such as lineament density, drainage density, slope, aspect, lithology and land use. These parameters were used to produce the thematic maps and weightage has been assigned to these thematic maps of parameters. Then, these thematic maps were reclassified and overlayed using the Weighted Overlay Method (WOM) in ArcGIS software. As a result, the study area is categorized into three landslide susceptibility zones: low, moderate, and high susceptibility zones. 5% of the study area is subject to low susceptibility to landslide, 71% of the study area is subject to moderate susceptibility to landslide, and 24% of the study area is subject to high susceptibility to landslide. Low susceptibility areas are primarily found on low land close to the town, such as Kampong Rawa, SMK Kuala Balah, and Tok Batin Forest Camp, meanwhile moderate susceptibility areas are located on valleys and low land and eventually high susceptibility areas are located on hilly and mountainous terrain area.
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Fang, Haoran, Yun Shao, Chou Xie, Bangsen Tian, Yu Zhu, Yihong Guo, Qing Yang, and Ying Yang. "Using Persistent Scatterer Interferometry for Post-Earthquake Landslide Susceptibility Mapping in Jiuzhaigou." Applied Sciences 12, no. 18 (September 14, 2022): 9228. http://dx.doi.org/10.3390/app12189228.
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Earthquakes cause a huge number of landslides and alter the regional landslide risk distribution. As a result, after a significant earthquake, the landslide susceptibility maps (LSMs) must be updated. The study goal was to create seismic landslide susceptibility maps containing landslide causative variables which are adaptable to great changes in susceptibility after the Jiuzhaigou earthquake (MS 7.0) and to perform a rapid update of the LSM after the earthquake by means of the distributed scatterer interferometric synthetic aperture radar (DS-InSAR) technique. We selected the territory of Jiuzhaigou County (southwestern China) as the study region. Jiuzhaigou is a world-renowned natural heritage and tourist area of great human and ecological value. For landslide susceptibility mapping, we examined the applicability of three models (logistic regression, support vector machine, and random forest) for landslide susceptibility mapping and offered a strategy for updating seismic landslide susceptibility maps using DS-InSAR. First, using logistic regression, support vector machine, and random forest techniques, susceptibility models of seismic landslides were built for Jiuzhaigou based on twelve contributing variables. Second, we obtained the best model parameters by means of a Bayesian network and network search, while using five-fold cross-validation to validate the optimized model. According to the receiver operating characteristic curve (ROC), the SVM model and RF model had excellent prediction capability and strong robustness over large areas compared with the LR models. Third, the surface deformation in Jiuzhaigou was calculated using DS-InSAR technology, and the deformation data were adopted to update the landslide susceptibility model using the correction matrix. The correction of deformation data resulted in a susceptibility class transition in 4.87 percent of the research region. According to practical examples, this method of correcting LSMs for the continuous monitoring of surface deformation (DS-InSAR) was effective. Finally, we analyze the reasons for the change in the revised LSM and point out the help of ecological restoration in reducing landslide susceptibility. The results show that the integration of InSAR continuous monitoring not only improved the performance of the LSM model but also adapted it to track the evolution of future landslide susceptibility, including seismic and human activities.
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Pham, Prakash, Chen, Ly, Ho, Omidvar, Tran, and Bui. "A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping." Sustainability 11, no. 22 (November 11, 2019): 6323. http://dx.doi.org/10.3390/su11226323.
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The main objective of this study is to propose a novel hybrid model of a sequential minimal optimization and support vector machine (SMOSVM) for accurate landslide susceptibility mapping. For this task, one of the landslide prone areas of Vietnam, the Mu Cang Chai District located in Yen Bai Province was selected. In total, 248 landslide locations and 15 landslide-affecting factors were selected for landslide modeling and analysis. Predictive capability of SMOSVM was evaluated and compared with other landslide models, namely a hybrid model of the cascade generalization optimization-based support vector machine (CGSVM), individual models, such as support vector machines (SVM) and naïve Bayes trees (NBT). For validation, different quantitative criteria such as statistical based methods and area under the receiver operating characteristic curve (AUC) technique were used. Results of the study show that the SMOSVM model (AUC = 0.824) has the highest performance for landslide susceptibility mapping, followed by CGSVM (AUC = 0.815), SVM (AUC = 0.804), and NBT (AUC = 0.800) models, respectively. Thus, the proposed novel SMOSVM model is a promising method for better landslide susceptibility mapping and prediction, which can be applied also in other landslide prone areas.
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Karakas, G., R. Can, S. Kocaman, H. A. Nefeslioglu, and C. Gokceoglu. "LANDSLIDE SUSCEPTIBILITY MAPPING WITH RANDOM FOREST MODEL FOR ORDU, TURKEY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 1229–36. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1229-2020.
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Abstract. Landslides are among commonly observed natural hazards all over the world and can be quite destructive for infrastructure and in settlement areas. Their occurrences are often related with extreme meteorological events and seismic activities. Preparation of landslide susceptibility maps is important for disaster mitigation efforts and to increase the resilience. The factors effective on landslide susceptibility map production depend mainly on the topography, land use and the geological characteristics of the region. The up-to-date and accurate data needed for extracting the effective parameters can be obtained by using photogrammetric techniques with high spatial resolution. Data driven ensemble methods are being increasingly used for landslide susceptibility map production and accurate results can be obtained. In this study, regional landslide susceptibility map of a landslide-prone area in a part of Ordu Province in northern Turkey is produced using topographic and lithological parameters by employing the random forest method. An actual landslide inventory delineated manually by geologists using the produced orthophotos and the digital terrain model (DTM) is used for training the model. The results show that an accuracy of 83% and precision of 92% can obtained from the data and the random forest method. The approach can be applied for generation of regional susceptibility maps semi-automatically.
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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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Zhao, Fumeng, Xingmin Meng, Yi Zhang, Guan Chen, Xiaojun Su, and Dongxia Yue. "Landslide Susceptibility Mapping of Karakorum Highway Combined with the Application of SBAS-InSAR Technology." Sensors 19, no. 12 (June 14, 2019): 2685. http://dx.doi.org/10.3390/s19122685.
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Geological conditions along the Karakorum Highway (KKH) promote the occurrence of frequent natural disasters, which pose a serious threat to its normal operation. Landslide susceptibility mapping (LSM) provides a basis for analyzing and evaluating the degree of landslide susceptibility of an area. However, there has been limited analysis of actual landslide activity processes in real-time. The SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) method can fully consider the current landslide susceptibility situation and, thus, it can be used to optimize the results of LSM. In this study, we compared the results of LSM using logistic regression and Random Forest models along the KKH. Both approaches produced a classification in terms of very low, low, moderate, high, and very high landslide susceptibility. The evaluation results of the two models revealed a high susceptibility of land sliding in the Gaizi Valley and the Tashkurgan Valley. The Receiver Operating Characteristic (ROC) curve and historical landslide verification points were used to compare the evaluation accuracy of the two models. The Area under Curve (AUC) value of the Random Forest model was 0.981, and 98.79% of the historical landslide points in the verification points fell within the range of high and very high landslide susceptibility degrees. The Random Forest evaluation results were found to be superior to those of the logistic regression and they were combined with the SBAS-InSAR results to conduct a new LSM. The results showed an increase in the landslide susceptibility degree for 2808 cells. We conclude that this optimized landslide susceptibility mapping can provide valuable decision support for disaster prevention and it also provides theoretical guidance for the maintenance and normal operation of KKH.
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Ado, Moziihrii, Khwairakpam Amitab, Arnab Kumar Maji, Elżbieta Jasińska, Radomir Gono, Zbigniew Leonowicz, and Michał Jasiński. "Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey." Remote Sensing 14, no. 13 (June 24, 2022): 3029. http://dx.doi.org/10.3390/rs14133029.
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Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.
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Deng, Hui, Xiantan Wu, Wenjiang Zhang, Yansong Liu, Weile Li, Xiangyu Li, Ping Zhou, and Wenhao Zhuo. "Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas." Remote Sensing 14, no. 17 (August 28, 2022): 4245. http://dx.doi.org/10.3390/rs14174245.
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Landslide susceptibility evaluation is critical for landslide prevention and risk management. Based on the slope unit, this study uses the information value method- random forest (IV-RF) model to evaluate the landslide susceptibility in the deep valley area. First, based on the historical landslide data, a landslide inventory was developed by using remote sensing technology (InSAR and optical remote sensing) and field investigation methods. Twelve factors were then selected as the input data for a landslide susceptibility model. Second, slope units with different scales were obtained by the r.slopeunits method and the information value method- random forest (IV-RF) model is used to evaluate the landslide susceptibility. Finally, the spatial distribution characteristics of landslide susceptibility grade under the optimal scale are analyzed. The results showed that under the slope unit obtained when c = 0.1 and a = 3 × 105 m2, the internal homogeneity/external heterogeneity of 8425 slope units extracted by the r.slopeunits method is the best, with an AUC of 0.905 and an F1 of 0.908. In this case, the accuracy of landslide susceptibility evaluation is the highest as well; it is shown that the finer slope units would not always lead to the higher accuracy of landslide susceptibility evaluation results; it is necessary to comprehensively consider the internal homogeneity and external heterogeneity of the slope units. Under the optimal slope unit scale, the number of landslides in the highly and extremely highly susceptible areas in the landslide susceptibility map accounted for 82.60% of the total number of landslides, which was consistent with the actual distribution of landslides; this study shows that the method, combining the slope unit and the information value method- random forest (IV-RF) model, for landslide susceptibility evaluation can obtain high accuracy.
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Trisnawati, D., Najib, A. S. Hidayatillah, A. Z. Robbany, and Y. A. L. Pellokila. "Comparative Study of Determination of Landslide Susceptibility Based on Weighting Methods and Analytical Hierarchy Processes in Pringapus, East Ungaran." IOP Conference Series: Earth and Environmental Science 1039, no. 1 (September 1, 2022): 012024. http://dx.doi.org/10.1088/1755-1315/1039/1/012024.
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Abstract Landslide is a geological disaster that is still an interesting topic to study its behavior and its management, especially for tropical climate regions such as Indonesia. One form of landslide countermeasures is the mapping of landslide susceptibility. The most commonly used landslide susceptibility mapping method is the GIS-based weighting and classification method. In the weighting method, each parameter and class has a definite weight as in the reference for making vulnerability maps that have been issued by BBSDLP (Center for Agricultural Land Resources) and PVMBG (Center for Volcanology and Geological Hazard Mitigation). However, the exact weight of each parameter sometimes does not match the real conditions in the field, so many researchers modify it. For this reason, this study tries to present an accuracy comparison of mapping the vulnerability of land motslide with the weighting method and the Analytical Hierarchy Process (AHP) method. The research location is in the Pringapus and East Ungaran areas, Semarang Regency. The results showed that the mapping of landslide susceptibility using the weighting method showed an accuracy of 77.58% while using the AHP method showed better accuracy of 84.48%.
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Ghorbanzadeh, Omid, Khalil Didehban, Hamid Rasouli, Khalil Valizadeh Kamran, Bakhtiar Feizizadeh, and Thomas Blaschke. "An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping." ISPRS International Journal of Geo-Information 9, no. 10 (September 27, 2020): 561. http://dx.doi.org/10.3390/ijgi9100561.
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In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor.
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Mirus, Benjamin B., Eric S. Jones, Rex L. Baum, Jonathan W. Godt, Stephen Slaughter, Matthew M. Crawford, Jeremy Lancaster, et al. "Landslides across the USA: occurrence, susceptibility, and data limitations." Landslides 17, no. 10 (May 29, 2020): 2271–85. http://dx.doi.org/10.1007/s10346-020-01424-4.
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Abstract Detailed information about landslide occurrence is the foundation for advancing process understanding, susceptibility mapping, and risk reduction. Despite the recent revolution in digital elevation data and remote sensing technologies, landslide mapping remains resource intensive. Consequently, a modern, comprehensive map of landslide occurrence across the United States (USA) has not been compiled. As a first step toward this goal, we present a national-scale compilation of existing, publicly available landslide inventories. This geodatabase can be downloaded in its entirety or viewed through an online, searchable map, with parsimonious attributes and direct links to the contributing sources with additional details. The mapped spatial pattern and concentration of landslides are consistent with prior characterization of susceptibility within the conterminous USA, with some notable exceptions on the West Coast. Although the database is evolving and known to be incomplete in many regions, it confirms that landslides do occur across the country, thus highlighting the importance of our national-scale assessment. The map illustrates regions where high-quality mapping has occurred and, in contrast, where additional resources could improve confidence in landslide characterization. For example, borders between states and other jurisdictions are quite apparent, indicating the variation in approaches to data collection by different agencies and disparity between the resources dedicated to landslide characterization. Further investigations are needed to better assess susceptibility and to determine whether regions with high relief and steep topography, but without mapped landslides, require further landslide inventory mapping. Overall, this map provides a new resource for accessing information about known landslides across the USA.
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Pradhan, Biswajeet, Zulkiflee Abd Latif, and Siti Nur Afiqah Aman. "Application of Airborne LiDAR-Derived Parameters and Probabilistic-Based Frequency Ratio Model in Landslide Susceptibility Mapping." Applied Mechanics and Materials 225 (November 2012): 442–47. http://dx.doi.org/10.4028/www.scientific.net/amm.225.442.
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The escalating number of occurrences of natural hazards such as landslides has raised a great interest among the geoscientists. Due to the extremely high number of point’s returns, airborne LiDAR permits the formation of more accurate DEM compared to other space borne and airborne remote sensing techniques. This study aims to assess the capability of LiDAR derived parameters in landslide susceptibility mapping. Due to frequent occurrence of landslides, Ulu Klang in Selangor state in Malaysia has been considered as application site. A high resolution of airborne LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive secondary deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI). A probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factors. Subsequently, factor ratings were summed up to yield Landslide Susceptibility Index (LSI) and finally a landslide susceptibility map was prepared. To test the model performance, receiver operating characteristics (ROC) curve was carried out together with area under curve (AUC) analysis. The produced landslide susceptibility map demonstrated that high resolution airborne LiDAR data has huge potential in landslide susceptibility mapping.
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Roodposhti, Majid, Jagannath Aryal, and Biswajeet Pradhan. "A Novel Rule-Based Approach in Mapping Landslide Susceptibility." Sensors 19, no. 10 (May 16, 2019): 2274. http://dx.doi.org/10.3390/s19102274.
Full textAbstract:
Despite recent advances in developing landslide susceptibility mapping (LSM) techniques, resultant maps are often not transparent, and susceptibility rules are barely made explicit. This weakens the proper understanding of conditioning criteria involved in shaping landslide events at the local scale. Further, a high level of subjectivity in re-classifying susceptibility scores into various classes often downgrades the quality of those maps. Here, we apply a novel rule-based system as an alternative approach for LSM. Therein, the initially assembled rules relate landslide-conditioning factors within individual rule-sets. This is implemented without the complication of applying logical or relational operators. To achieve this, first, Shannon entropy was employed to assess the priority order of landslide-conditioning factors and the uncertainty of each rule within the corresponding rule-sets. Next, the rule-level uncertainties were mapped and used to asses the reliability of the susceptibility map at the local scale (i.e., at pixel-level). A set of If-Then rules were applied to convert susceptibility values to susceptibility classes, where less level of subjectivity is guaranteed. In a case study of Northwest Tasmania in Australia, the performance of the proposed method was assessed by receiver operating characteristics’ area under the curve (AUC). Our method demonstrated promising performance with AUC of 0.934. This was a result of a transparent rule-based approach, where priorities and state/value of landslide-conditioning factors for each pixel were identified. In addition, the uncertainty of susceptibility rules can be readily accessed, interpreted, and replicated. The achieved results demonstrate that the proposed rule-based method is beneficial to derive insights into LSM processes.
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Zhou, Suhua, Shuaikang Zhou, and Xin Tan. "Nationwide Susceptibility Mapping of Landslides in Kenya Using the Fuzzy Analytic Hierarchy Process Model." Land 9, no. 12 (December 21, 2020): 535. http://dx.doi.org/10.3390/land9120535.
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Landslide susceptibility mapping (LSM) is a cost-effective tool for landslide hazard mitigation. To date, no nationwide landslide susceptibility maps have been produced for the entire Kenyan territory. Hence, this work aimed to develop a landslide susceptibility map at the national level in Kenya using the fuzzy analytic hierarchy process method. First, a hierarchical evaluation index system containing 10 landslide contributing factors and their subclasses was established to produce a susceptibility map. Then, the weights of these indexes were determined through pairwise comparisons, in which triangular fuzzy numbers (TFNs) were employed to scale the relative importance based on the opinions of experts. Ultimately, these weights were merged in a hierarchical order to obtain the final landslide susceptibility map. The entire Kenyan territory was divided into five susceptibility levels. Areas with very low susceptibility covered 5.53% of the Kenyan territory, areas with low susceptibility covered 20.58%, areas with the moderate susceptibility covered 29.29%, areas with high susceptibility covered 29.16%, and areas with extremely high susceptibility covered 15.44% of Kenya. The resulting map was validated using an inventory of 425 historical landslides in Kenya. The results indicated that the TFN-AHP model showed a significantly improved performance (AUC = 0.86) compared with the conventional AHP (AUC = 0.72) in LSM for the study area. In total, 31.53% and 29.88% of known landslides occurred within the “extremely high” and “high” susceptibility zones, respectively. Only 8.24% and 1.65% of known landslides fell within the “low” and “very low” susceptibility zones, respectively. The map obtained as a result of this study is beneficial to inform planning and land resource management in Kenya.
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Camarinha, P. I. M., V. Canavesi, and R. C. S. Alvalá. "Shallow landslide prediction and analysis with risk assessment using a spatial model in a coastal region in the state of São Paulo, Brazil." Natural Hazards and Earth System Sciences 14, no. 9 (September 17, 2014): 2449–68. http://dx.doi.org/10.5194/nhess-14-2449-2014.
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Abstract. This study presents a methodology for susceptibility mapping of shallow landslides just from data and software from the public domain. The study was conducted in a mountainous region located on the southeastern Brazilian coast, in the state of São Paulo. The proposal is that the methodology can be replicated in a practical and reliable way in several other municipalities that do not have such mappings and that often suffer from landslide-related disasters. The susceptibility mapping was generated based on the following maps: geological, soils, slope, horizontal and vertical curvatures, and land use. The thematic classes of these maps were weighted according to technical and scientific criteria related to the triggering of landslides, and were crossed by the fuzzy gamma technique. The mapping was compared with the risk sector survey made by the Brazilian Geological Survey (CPRM), which is the official database used by municipalities and civil defense in risk management. The results showed positive correlations, so that the critical risk sectors had higher proportions for the more susceptible classes. To compare the approach with other studies using landslide-scar maps, correlated indices were evaluated, which also showed satisfactory results, thus indicating that the methodology presented is appropriate for risk assessment in urban areas.
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Kavitha, G., S. Anbazhagan, and S. Mani. "Geospatial Technology for Landslide Susceptibility Mapping along the Vathalmalai Ghat road section, South India." Journal of Geology, Geography and Geoecology 30, no. 4 (December 25, 2021): 683–91. http://dx.doi.org/10.15421/112163.
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Landslides are among the most prevalent and harmful hazards. Assessment of landslide susceptibility zonation is an important task in reducing the losses of lifeand properties. The present study aims to demarcate the landslide prone areas along the Vathalmalai Ghat road section (VGR) using remote sensing and GIS techniques. In the first step, the landslide causative factors such as geology, geomorphology, slope, slope aspect, land use / land cover, drainage density, lineament density, road buffer and relative relief were assessed. All the factors were assigned to rank and weight based on the slope stability of the landslide susceptibility zones. Then the thematic maps were integrated using ArcGIS tool and landslide susceptibility zonation was obtained and classified into five categories ; very low, low, moderate, high and very high. The landslide susceptibility map is validated with R-index and landslide inventory data collected from the field using GPS measurement. The distribution of susceptibility zones is ; 16.5% located in very low, 28.70% in low, 24.70% in moderate, 19.90% in high and 10.20% in very high zones. The R-index indicated that about 64% landslide occurences correlated with high to very high landslide susceptiblity zones. The model validation indicated that the method adopted in this study is suitable for landslide disaster mapping and planning.
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Djokanovic, Sonja. "Landslide susceptibility mapping of SE Serbia using GIS." Annales g?ologiques de la Peninsule balkanique 80, no. 2 (2019): 105–16. http://dx.doi.org/10.2298/gabp1902105d.
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Landslides represent a great problem in Serbia. According to current estimates 30-35 % of Serbia is affected by landslides. In this paper a landslide susceptibility analysis is done for SE Serbia. Study area covers 1507 km2. Relief is hilly or mountainous and characterized by high altitude differences. Analysis is done by geographic information system (GIS) and evaluation by analytic hierarchy process (AHP). For susceptibility assessment are used four factors: lithology, slope angle, distance to rivers and distance to faults. The most landslides are formed on slope steepness less than 30?. There is four classes of susceptibility in study area. Zone of very high susceptibility make 63.9 % of the study area. Zone of high susceptibility covers 15.7 % of the study area. The moderate class occupies 37.4% and zone classified as having low susceptibility accounts for 10 % of study area. Final landslide susceptibility map of SE Serbia is satisfactory.
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Bachri, Syamsul, Rajendra P. Shrestha, Fajar Yulianto, Sumarmi Sumarmi, Kresno Sastro Bangun Utomo, and Yulius Eka Aldianto. "Mapping Landform and Landslide Susceptibility Using Remote Sensing, GIS and Field Observation in the Southern Cross Road, Malang Regency, East Java, Indonesia." Geosciences 11, no. 1 (December 24, 2020): 4. http://dx.doi.org/10.3390/geosciences11010004.
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There has been an increasing trend of land area being brought under human’s use over time. This situation has led the community to carry out land-use development activities in landslide hazard-prone areas. The use of land can have a positive impact by increasing economic conditions, but it can have negative impacts on the environment. Therefore, this study aimed to identify the landslide hazard, focusing on the development of a landform map to reduce the risk of landslide disaster in JLS, Malang Regency. The integration of remote sensing and geographic information systems, as well as field observation, were used to create a landform map and a landslide susceptibility map. Using the geomorphological approach as a basic concept in landform mapping, the morphology, morphogenesis, and morphoarrangement conditions were obtained from the remote sensing data, GIS, and field observation, while morphochronological information was obtained from a geological map. The landslide susceptibility map was prepared using 11 landslide conditioning factors by employing the index of entropy method. Thirty-nine landform units were successfully mapped into four landslide susceptibility classes. The results showed that the study area is dominated by a high level of landslide susceptibility with a majority of moderate to strongly eroded hill morphology. It also reaffirms that landform mapping is a reliable method by which to investigate landslide susceptibility in JLS, Malang Regency.
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Ulakpa, R. O. E., V. U. D. Okwu, K. E. Chukwu, and M. O. Eyankware. "LANDSLIDE SUSCEPTIBILITY MODELLING IN SELECTED STATES ACROSS SE. NIGERIA." Environment & Ecosystem Science 4, no. 1 (March 30, 2020): 23–27. http://dx.doi.org/10.26480/ees.01.2020.23.27.
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Identification and mapping of landslide is essential for landslide risk and hazard assessment. This paper gives information on the uses of landsat imagery for mapping landslide areas ranging in size from safe area to highly prone areas. Landslide mitigation largely depends on the understanding of the nature of the factors namely: slope, soil type, lineament, lineament density, elevation, rainfall and vegetation. These factors have direct bearing on the occurrence of landslide. Identification of these factors is of paramount importance in setting out appropriate and strategic landslides control measures. Images for this study was downloaded by using remote sensing with landsat 8 ETM and aerial photos using ArcGIS 10.7 and Surfer 8 software, while Digital Elevation Model (DEM) and Google EarthPro TM were used to produce slope, drainage, lineament and elevation. From the processed landsat 8 imagery, landslide susceptibility map was produced, and landslide was category into various class; low, medium and high. From the study, it was observed that Enugu and Anambra state ranges from high to medium in terms of landslide susceptibility, Imo state ranges from medium to low.