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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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Bhandary, Netra Prakash, Ranjan Kumar Dahal, Manita Timilsina, and Ryuichi Yatabe. "Rainfall event-based landslide susceptibility zonation mapping." Natural Hazards 69, no. 1 (May 18, 2013): 365–88. http://dx.doi.org/10.1007/s11069-013-0715-x.
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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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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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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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Shrestha, Him Lal, and Mahesh Poudel. "Landslide Susceptibility Zonation Mapping in Post- Earthquake Scenario in Gorkha District." Forestry: Journal of Institute of Forestry, Nepal 15 (July 31, 2018): 45–56. http://dx.doi.org/10.3126/forestry.v15i0.24920.
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Landslide hazard zonation map is prepared to assist planners to implement mitigation measures so that further damage and loss can be minimized. In this study, post 25 April 2015 earthquake remote sensing data were used to prepare landslide inventory. Landsat images after the earthquake were downloaded from the National Aeronautics and Space Administration (NASA) website and processed using ArcGIS, ERDAS imagine and Analytical Hierarchy Process (AHP) as an extension in ArcGIS. The study was carried out in Gorkha district as this was the epicenter of the main earthquake of 25 April 2015 and consequently was highly affected by earthquake triggered landslide. The digital imagery was processed to analyze land use/land cover type. Geological features were analyzed using the criteria like color, tone, topography, stream drainage, etc. Primary topographic features like slope, aspect, elevation, etc. were generated from Digital Elevation Model (DEM). Seismological data (magnitude and epicenter) were obtained from Department of Seismology. For Landslide Susceptibility Zonation (LSZ) different thematic maps like Land Use and Land Cover (LULC) map, slope map, aspect map, lithological map, buffer map (distance from road and river/water source), soil map, and seismological map were assigned relative weights on the ordinal scale to obtain Landslide Susceptibility Index (LSI). Threshold values were selected according to breaks in LSI frequency and a LSZ map was prepared which shows very low, low, moderate, high, very high hazard zones in Gorkha district.
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Das, Jayshree, Susanta Mahato, Pawan Kumar Joshi, and Yuei-An Liou. "Forest Fire Susceptibility Zonation in Eastern India Using Statistical and Weighted Modelling Approaches." Remote Sensing 15, no. 5 (February 27, 2023): 1340. http://dx.doi.org/10.3390/rs15051340.
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Recurring forest fires disturb ecological balance, impact socio-economic harmony, and raise global concern. This study implements multiple statistical and weighted modelling approaches to identify forest fire susceptibility zones in Eastern India. Six models, namely, Frequency Ratio (FR), Certainty Factor (CF), Natural Risk Factor (NRF), Bivariate statistical (Wi and Wf), Analytical Hierarchy Process (AHP), and Logistic Regression (LR) were used in the study. Forest fire inventory (2001 to 2018) mapping was done using forest fire points captured by the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Fire responsible components, namely, topography (which has four variables), climate (5), biophysics (8) and disturbance (4) were used as inputs to the modelling approaches. Multicollinearity analysis was carried out to examine the association and remove the highly-correlated variables before performing the modeling. Validation of model prediction levels was done using Area Under the Receiver Operating Characteristic Curve (ROC curve-AUC) value. The results reveal that the areas with west and southwest orientations, and moderate slope demarcate higher susceptibility to forest fire. High precipitation areas with lower temperature but ample solar radiation increase their susceptibility to forest fire. Mixed deciduous forest type with ample solar radiation, higher NDVI, lower NDWI and lower TWI values exhibits higher susceptibility. Model validation shows that LR (with AUC = 0.809) outperforms other models used in the study. To minimize the risk of fire and frame with proper management plans for the study area, susceptibility mapping using satellite imageries, GIS technique, and modelling approaches is highly recommended.
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Basnet, P., M. K. Balla, and B. M. Pradhan. "Landslide hazard zonation, mapping and investigation of triggering factors in Phewa lake watershed, Nepal." Banko Janakari 22, no. 2 (December 1, 2013): 43–52. http://dx.doi.org/10.3126/banko.v22i2.9198.
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The landslide triggering factors were investigated followed by the thematic maps and landslide distribution map prepared and classified using the GPS and GIS Softwares like CartaLinx, ArcView and ERDAS IMAGINE in Sarangkot and Kaskikot Village Development Committees, Kaski district. In analytical hierarchy process, the factors for zonation were compared by Couple Comparison Method and their weights were determined using Arithmetic Mean Method and earned weight values of each factor. The landslide hazard zonation model was employed to prepare landslide hazard zonation map of the study area, and then classified into five relative hazard classes using the equal interval classification method. Finally, the landslide hazard zonation map was crossed with the landslide distribution map and the model applicability was confirmed by determining the per hazard class percent of area covered by the landslide. In the land hazard zonation map, 0.44% of the study area was in very low hazard, 2.11% in low hazard, 54.92% in moderate hazard, 21.34% in high hazard and 21.19% in very high hazard area. The major portion of the study area was on the moderate zone whereas the least portion was on the very low hazard zone. In the study area, most of the high and very high hazard class areas were found occupying the areas closer to the linear triggering factors like presence of linement and fault, presence of motorable road and presence of rivers and streams. The landslide density of the study area was found to be 0.44 per km2 indicating the higher hazard susceptibility of the area.DOI: http://dx.doi.org/10.3126/banko.v22i2.9198Banko Janakari: A Journal of Forestry Information for NepalVol. 22, No. 2, 2012 November Page: 43-52 Uploaded date: 12/1/2013
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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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Rossi, Mauro, Txomin Bornaetxea, and Paola Reichenbach. "LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation." Geoscientific Model Development 15, no. 14 (July 21, 2022): 5651–66. http://dx.doi.org/10.5194/gmd-15-5651-2022.
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Abstract. In the past 50 years, a large variety of statistically based models and methods for landslide susceptibility mapping and zonation have been proposed in the literature. The methods, which are applicable to a large range of spatial scales, use a large variety of input thematic data, different model combinations, and several approaches to evaluate the models' performance. Despite the numerous applications available in the literature, a standard approach for susceptibility modeling and zonation is still missing. The literature search revealed that several software program and tools are available to evaluate regional slope stability using physically based analysis, but only a few use statistically based approaches. Among them, LAND-SE (LANDslide Susceptibility Evaluation) provides the possibility to perform and combine different statistical susceptibility models and to evaluate their performances and associated uncertainties. This paper describes the structure and the functionalities of LAND-SUITE, a suite of tools for statistically based landslide susceptibility modeling which integrates LAND-SE. LAND-SUITE completes and extends LAND-SE, adding functionalities to (i) facilitate input data preparation, (ii) perform preliminary and exploratory analysis of the available data, and (iii) test different combinations of variables and select the optimal thematic/explanatory set. LAND-SUITE provides a tool to assist the user during the data preparatory phase and to perform diversified statistically based landslide susceptibility applications.
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Capitani, Marco, Adriano Ribolini, and Monica Bini. "Susceptibility to Translational Slide-Type Landslides: Applicability of the Main Scarp Upper Edge as a Dependent Variable Representation by Reduced Chi-Square Analysis." ISPRS International Journal of Geo-Information 7, no. 9 (August 22, 2018): 336. http://dx.doi.org/10.3390/ijgi7090336.
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The applicability of main scarp upper edge (MSUE) as dependent variable representation was performed in a translational slide susceptibility zonation of the Milia and Roglio basins, Italy. Two landslide inventories were built thanks to detailed geomorphological mapping and aerial photograph analysis. The landslides were used to create the models before 1975, while those after 1975 were employed to validate the predictive power of the model. Possible landslide-related factors were chosen from a geomorphological survey. The inventory landslide maps and the landslide-related factor maps were processed by conditional analysis, producing landslide susceptibility maps with five susceptibility classes. A comparison between the distribution of landslides after 1975 and those derived from models provided the predictive power of each model, which in turn was used to define the best predictive model. Reduced chi-square analysis allowed to define the efficiency of MSUE as dependent variable representation. MSUE can be applied as dependent variable representation to landslide susceptibility zonation with appreciable results. In the Roglio basin, slope angle, distance from streams, and from tectonic lineaments proved to be the main controlling factors of translational slides, whereas in the Milia basin, lithology and slope angle gave more satisfactory results as landslide-predisposing factors.
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Chawla, Amit, Srinivas Pasupuleti, Sowmiya Chawla, A. C. S. Rao, Kripamoy Sarkar, and Rajesh Dwivedi. "Landslide Susceptibility Zonation Mapping: A Case Study from Darjeeling District, Eastern Himalayas, India." Journal of the Indian Society of Remote Sensing 47, no. 3 (January 1, 2019): 497–511. http://dx.doi.org/10.1007/s12524-018-0916-6.
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Kouli, M., C. Loupasakis, P. Soupios, D. Rozos, and F. Vallianatos. "Comparing multi-criteria methods for landslide susceptibility mapping in Chania Prefecture, Crete Island, Greece." Natural Hazards and Earth System Sciences Discussions 1, no. 1 (January 29, 2013): 73–109. http://dx.doi.org/10.5194/nhessd-1-73-2013.
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Abstract. In this work, two multi-criteria methods, an expert-based, semi-quantitative, relative weighting – rating approach, the weighted linear combination (WLC) and a quantitative, statistical method, the weights of evidence (WoE) approach were applied for landslide susceptibility zonation mapping in the Chania Prefecture of Crete Island, Greece. Several thematic maps representing various landslide casual factors, such as geological formations, faults proximity, elevation, slope gradient, aspect and curvature, rivers proximity, precipitation, roads proximity and land use types; have been generated in a GIS environment. Two landslide susceptibility maps were created; one for each method. The maps were compared and validated using the success rate curve (SRC) analysis. The resulting landslide susceptibility maps have uncertainties introduced due to the subjective knowledge of experts in the case of WLC method and to the quality of the recorded landslides sample in the case of the WoE method. Both approaches produced almost equally accurate maps with the WoE method to produce slightly superior predictions.
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Rossi, Mauro, and Paola Reichenbach. "LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0." Geoscientific Model Development 9, no. 10 (October 4, 2016): 3533–43. http://dx.doi.org/10.5194/gmd-9-3533-2016.
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Abstract. Landslide susceptibility (LS) assessment provides a relative estimate of landslide spatial occurrence based on local terrain conditions. A literature review revealed that LS evaluation has been performed in many study areas worldwide using different methods, model types, different partition of the territory (mapping units) and a large variety of geo-environmental data. Among the different methods, statistical models have been largely used to evaluate LS, but the minority of articles presents a complete and comprehensive LS assessment that includes model performance analysis, prediction skills evaluation, and estimation of the errors and uncertainty. The aim of this paper is to describe LAND-SE (LANDslide Susceptibility Evaluation) software that performs susceptibility modelling and zonation using statistical models, quantifies the model performances, and the associated uncertainty. The software is implemented in R, a free software environment for statistical computing and graphics. This provides users with the possibility to implement and improve the code with additional models, evaluation tools, or output types. The paper describes the software structure, explains input and output, and illustrates specific applications with maps and graphs. The LAND-SE script is delivered with a basic user guide and three example data sets.
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Van Den Eeckhaut, M., P. Reichenbach, F. Guzzetti, M. Rossi, and J. Poesen. "Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium." Natural Hazards and Earth System Sciences 9, no. 2 (March 31, 2009): 507–21. http://dx.doi.org/10.5194/nhess-9-507-2009.
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Abstract. For a 277 km2 study area in the Flemish Ardennes, Belgium, a landslide inventory and two landslide susceptibility zonations were combined to obtain an optimal landslide susceptibility assessment, in five classes. For the experiment, a regional landslide inventory, a 10 m × 10 m digital representation of topography, and lithological and soil hydrological information obtained from 1:50 000 scale maps, were exploited. In the study area, the regional inventory shows 192 landslides of the slide type, including 158 slope failures occurred before 1992 (model calibration set), and 34 failures occurred after 1992 (model validation set). The study area was partitioned in 2.78×106 grid cells and in 1927 topographic units. The latter are hydro-morphological units obtained by subdividing slope units based on terrain gradient. Independent models were prepared for the two terrain subdivisions using discriminant analysis. For grid cells, a single pixel was identified as representative of the landslide depletion area, and geo-environmental information for the pixel was obtained from the thematic maps. The landslide and geo-environmental information was used to model the propensity of the terrain to host landslide source areas. For topographic units, morphologic and hydrologic information and the proportion of lithologic and soil hydrological types in each unit, were used to evaluate landslide susceptibility, including the depletion and depositional areas. Uncertainty associated with the two susceptibility models was evaluated, and the model performance was tested using the independent landslide validation set. An heuristic procedure was adopted to combine the landslide inventory and the susceptibility zonations. The procedure makes optimal use of the available landslide and susceptibility information, minimizing the limitations inherent in the inventory and the susceptibility maps. For the established susceptibility classes, regulations to link terrain domains to appropriate land rules are proposed.
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Malsawmtluanga and Ch Vabeihmo. "Assessment of Flood Hazard Zonation Using Geographic Information System and Analytical Hierarchy Process: A Case Study of Tlawng River Watershed in Sairang, Mizoram, India." Nature Environment and Pollution Technology 21, no. 1 (March 6, 2022): 101–9. http://dx.doi.org/10.46488/nept.2022.v21i01.011.
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Flood occurs when the water inundates normally dry ground, which could happen in a variety of ways like excessive rainfall, overflowing of embankments, dams, rivers, snowmelt, and other factors. Floods are one form of a natural hazard which are difficult to contain and control. A flood susceptibility mapping using Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) techniques were carried out at Sairang village in Aizawl, Mizoram in Northeast India. The study area Sairang is situated on the banks of the Tlawng river, the longest river in Mizoram. Floods have wreaked havoc in Sairang frequently resulting in huge losses and damage to property with numerous loss of life over the years. The total study area is 131.27 sq km and the resulting flood hazard potential zonation map shows that 1/3 of the watershed area falls in Vey High and High Potential Flood Hazard Zonation areas.
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Pavel, Mihai, John D. Nelson, and R. Jonathan Fannin. "An analysis of landslide susceptibility zonation using a subjective geomorphic mapping and existing landslides." Computers & Geosciences 37, no. 4 (April 2011): 554–66. http://dx.doi.org/10.1016/j.cageo.2010.10.006.
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Thanh, Long Nguyen, Yao-Min Fang, Tien-Yin Chou, Thanh-Van Hoang, Quoc Dinh Nguyen, Chen-Yang Lee, Chin-Lun Wang, Hsiao-Yuan Yin, and Yi-Chia Lin. "Using Landslide Statistical Index Technique for Landslide Susceptibility Mapping: Case Study: Ban Khoang Commune, Lao Cai Province, Vietnam." Water 14, no. 18 (September 9, 2022): 2814. http://dx.doi.org/10.3390/w14182814.
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Ban Khoang is a mountainous commune in Sa Pa district located in the central part of Lao Cai province, Vietnam. Landslides occur frequently in this area and seriously affect the local living conditions. To help the local authority in developing a landslide disaster action plan, the statistical index method for landslide susceptibility mapping is applied. As the result, the landslide susceptibility zonation (LSZ) map was created. The LSZ map indicates that areas of low, moderate, high and very high landslide susceptibility zones are, respectively, 20.3 km2, 12.4 km2, 15.4 km2, and 5.2 km2; most of the observed landslide areas that are well predicted belong to high or very high landslide susceptibility classes. In detail, 80% observed landslide areas and 78.57% number of observed landslides were well predicted, and the area (AUC) under the receiver operating characteristic (ROC) curve obtained 80.3%. Hence, the high and very high landslide susceptibility classes in the LSZ map can be considered highly believable, and the LSZ map will be reliable to use in the practice.
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Yang, Xiaojie, Zhenli Hao, Keyuan Liu, Zhigang Tao, and Guangcheng Shi. "An Improved Unascertained Measure-Set Pair Analysis Model Based on Fuzzy AHP and Entropy for Landslide Susceptibility Zonation Mapping." Sustainability 15, no. 7 (April 4, 2023): 6205. http://dx.doi.org/10.3390/su15076205.
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Landslides are one of the most destructive and common geological disasters in the Tonglvshan mining area, which seriously threatens the safety of surrounding residents and the Tonglvshan ancient copper mine site. Therefore, to effectively reduce the landslide risk and protect the safety of the Tonglvshan ancient copper mine site, it is necessary to carry out a systematic assessment of the landslide susceptibility in the study area. Combining the unascertained measure (UM) theory, the dynamic comprehensive weighting (DCW) method based on the fuzzy analytic hierarchy process (AHP)-entropy weight method and the set pair analysis (SPA) theory, an improved UM-SPA coupling model for landslide susceptibility assessment is proposed in this study. First, a hierarchical evaluation index system including 10 landslide conditioning factors is constructed. Then, the dynamic comprehensive weighting method based on the fuzzy AHP-entropy weight method is used to assign independent comprehensive weights to each evaluation unit. Finally, we optimize the credible degree recognition criteria of UM theory by introducing SPA theory to quantitatively determine the landslide susceptibility level. The results show that the improved UM-SPA model can produce landslide susceptibility zoning maps with high reliability. The whole study area is divided into five susceptibility levels. 5.8% and 10.16% of the Tonglvshan mining area are divided into extremely high susceptibility areas and high susceptibility areas, respectively. The low and extremely low susceptibility areas account for 30.87% and 34.14% of the total area of the study area, respectively. Comparison with the AHP and Entropy-FAHP models indicates that the improved UM-SPA model (AUC = 0.777) shows a better performance than the Entropy-FAHP models (AUC = 0.764) and the conventional AHP (AUC = 0.698). Therefore, these results can provide reference for emergency planning, disaster reduction and prevention decision-making in the Tonglvshan mining area.
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Zumpano, Veronica, Luca Pisano, Francesco Filice, Angelo Ugenti, Daniela de Lucia, Janusz Wasowski, Francesca Santaloia, and Piernicola Lollino. "Regional-Scale Seismic Liquefaction Susceptibility Mapping via an Empirical Approach Validated by Site-Specific Analyses." Geosciences 12, no. 5 (May 17, 2022): 215. http://dx.doi.org/10.3390/geosciences12050215.
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Regional-scale analyses of susceptibility to liquefaction are seldom performed in data-scarce areas. However, in spite of data limitations, such efforts can still provide useful information in case of populated, seismically active regions. The present work focuses on susceptibility zonation for soil liquefaction that might occur due to ground shaking in the Foggia Province, a data-scarce, seismically active area of about 7000 km2 located in southern Italy. The Analytical Hierarchy Process (AHP) approach is used to obtain the susceptibility to liquefaction map of the whole area, while a geological and geotechnical database including 531 boreholes from 84 localities is used for cross-validation. The data are processed by means of a simplified quantitative method to determine liquefaction potential and assess whether a specific area is prone to liquefaction or not. Our results, along with an AUC − ROC = 0.89 test value, indicate that there are widespread areas of medium to high and very high susceptibility, and that the most susceptible zones are localized along the Adriatic Sea coastline and watercourses. The final susceptibility to liquefaction map represents a step forward towards the assessment of secondary seismic hazard in the study area, thus supporting the regional and local administrations responsible for land-use planning and risk mitigation.
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Karande, Subhash Vishnu. "Landslide Hazard Zonation along the MH SH-73 at Kelghar Ghat, Satara, Maharashtra." International Journal of Research and Review 8, no. 9 (September 29, 2021): 540–47. http://dx.doi.org/10.52403/ijrr.20210968.
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Road transportation is the most common victim of landslide in the world. The present study investigates the landslide hazard zonation along the MH SH-73 at Kelghar ghat between Medha and Mahabaleshwar hill station of Maharashtra. Remote Sensing and GIS were used for the landslide hazard zonation of this section. The ghat section was buffered 100 m on both side to define the extent of study area based on the field investigation. The study incorporated predefined important landslide causative factors, viz. slope, rainfall, relief, lithology, soil depth, soil erosion, soil texture, land use / land cover, drainage distance, drainage density, lineament distance, lineament density, aspect, temperature, landslide inventory and in this approach fifteen thematic layers were prepared in GIS platform. The weight and score were assigned to each thematic layer based on heuristic approach on their relative importance in causing landslide. Multi-criteria model in ArcGIS 10.5 software were used for the mapping landslide hazard zones and it were classified into six zones: very high (1.3 %), high (1.7 %), moderate (3.4 %), low (7.6 %) very low (8.4 %) and no risk zone (77.7 %). The final result of this research can help for proper mitigation and adaptation measures for engineers, planners and administrators for this ghat section. Keywords: GIS, Landslide Hazard Zonation, Remote Sensing, Susceptibility, Sahyadri, Kelghar.
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Gadtaula, Arishma, and Subodh Dhakal. "Landslide susceptibility mapping using Weight of Evidence Method in Haku, Rasuwa District, Nepal." Journal of Nepal Geological Society 58 (June 25, 2019): 163–71. http://dx.doi.org/10.3126/jngs.v58i0.24601.
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The 2015 Gorkha Earthquake resulted in many other secondary hazards affecting the livelihoods of local people residing in mountainous area. Plenty of earthquake induced landslides and mass movement activities were observed after earthquake. Haku region of Rasuwa was also one of the severely affected areas by co-seismic landslides triggered by the disastrous earthquake. Statistics shows that around 400 families were relocated from Haku Post-earthquake (MoFA, 2015). A total of 101 co-seismic landslides were focused during the study and were verified during the fieldwork in Haku village. The conditioning factors used in this study were slope, aspect, elevation, curvature (plan and profile), landuse, geology and PGA. The conditioning factor maps were prepared in GIS working environment and further analysis was conducted with the assistance of Google earth. This study used Weight of Evidence (WoE), a bivariate statistical model and its performance was assessed. The susceptibility map was further characterized into five different classes namely very low, low, high, medium and very high susceptibility zones. The statistical analysis obtained from the results of the susceptibility map prepared by using WoE model gave the results that maximum area percentage of landslide distribution was observed in medium and high susceptibility classes i.e. 38% and 33% followed by very high (13%), low (10%) and very low classes (5.8%) About 25% of the total landslides are separated to validate the prepared model used in the landslide susceptibility zonation. The overlay method predicts the reliability of the model.
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Suh, Jangwon. "An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence." Applied Sciences 10, no. 21 (November 5, 2020): 7845. http://dx.doi.org/10.3390/app10217845.
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This article reviews numerous published studies on geographic information system (GIS)-based assessment and mapping of mining-induced subsidence. The various types of mine subsidence maps were first classified into susceptibility, hazard, and risk maps according to the various types of the engineering geology maps. Subsequently, the mapping studies were also reclassified into several groups according to the analytic methods used in the correlation derivation or elements of the risk of interest. Data uncertainty, analytic methods and techniques, and usability of the prediction map were considered in the discussion of the limitations and future perspectives of mining subsidence zonation studies. Because GIS can process geospatial data in relation to mining subsidence, the application and feasibility of exploiting GIS-assisted geospatial predictive mapping may be expanded further. GIS-based subsidence predictive maps are helpful for both engineers and for planners responsible for the design and implementation of risk mitigation and management strategies in mining areas.
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Tohari, Adrin. "Seismic microzonation of soil amplification and liquefaction for Padang City." E3S Web of Conferences 156 (2020): 02008. http://dx.doi.org/10.1051/e3sconf/202015602008.
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The magnitude 7.6 MW earthquake that occurred on 30 September 2009 in West Sumatera caused significant damages to buildings in the city of Padang related to the phenomenon of amplification and liquefaction. This paper presents the results of the assessment and mapping of amplification and liquefaction, carried out in the coastal area of Padang City. Mapping of soil amplification was carried out in 250 locations using the HVSR microtremor method. Meanwhile, evaluation of the potential for liquefaction was carried out in 95 locations using a cone penetration test-based method. Based on the analysis, Padang City has five seismic susceptibility zonations. Coastal areas, including the sub-districts of Koto Tangah, North Padang, West Padang, and South Padang, are located in high to very high susceptibility to soil amplification and liquefaction. These results are in agreement with the phenomenon of building damage due to amplification and liquefaction during the 2009 earthquake.
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Ahyuni, Ahyuni, Bigharta Bekti Susetyo, Isra Haryati Diva, Zakiyah Mar’ah, Hamdi Nur, Adenan Yandra Nofrizal, and Azwirda Aziz. "Landslides Susceptibility Mapping in R Program (Case study in Lima Puluh Kota Regency)." Malaysian Journal of Fundamental and Applied Sciences 18, no. 2 (May 16, 2022): 271–82. http://dx.doi.org/10.11113/mjfas.v18n2.2534.
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Landslide susceptibility zonation is necessary to be considered in land use planning at various scales., different approaches and analytical methods can be used to evaluate and zone the area and processed with GIS software. However, there are constraints in its use, such as the cost of the licenses of software and source code that cannot be accessed and evaluated by users. The recent development of open-source software that can integrate data, analysis, and graphs in a representation such as the R program, has opened up opportunities for researchers to reevaluate and modify interpretation further from available ones to address issues. In this regard, this study aims to create functions in R using the Weight of Evidence (WoE) method, a form of bivariate statistic approach to acquire the significant factors controlling landslides and generate a susceptibility map. The case study is located in Limapuluh Kota Regency, West Sumatra Province of Indonesia, a hilly and mountainous region where its districts are prone to landslides. Eight of eleven factors such as geology, landform, land cover, elevation, density of vegetation greenness, slope, rainfall intensity, and proximity to stream were regarded to control landslides which set up four classes of landslide susceptibility zone (very low, low, moderate, and high).
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Quesada Román, Adolfo. "Landslides and floods zonation using geomorphological analyses in a dynamic basin of Costa Rica." Revista Cartográfica, no. 102 (January 4, 2021): 125–38. http://dx.doi.org/10.35424/rcarto.i102.901.
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Despite of the geomorphological diversity of Central America and Costa Rica, there are few detailed geomorphological studies in the region. A 1:25,000 geomorphological analyses of the Upper General River Basin (UGRB) located in the southeast in Costa Rica is presented, based on the interpretation of aerial photographs and field geomorphological mapping. First, a morphometric analysis was performed to calculate and analyze seven variables that were merged in order to produce the flood and landslides susceptibility maps. Second, a total of 43 types of landforms divided genetically into endogenic (tectonic), and exogenic (fluvial, gravitational, and glacial) features are mapped for an area of 1560 km2. Finally, a geomorphological hazard map with the zonation of the different susceptibility levels of landslides and floods were performed. This cartography is important in terms of geomorphological evolution, disaster risk reduction as well as for land use planning for approximately 40,000 inhabitants. The presented methodology can also be applied in other developing countries for different purposes such as landscape evolution, morphogenetic detailed maps, disaster risk reduction, and land use planning.
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Schlegel, Tobias U., Renee Birchall, Tina D. Shelton, and James R. Austin. "MAPPING THE MINERAL ZONATION AT THE ERNEST HENRY IRON OXIDE COPPER-GOLD DEPOSIT: VECTORING TO Cu-Au MINERALIZATION USING MODAL MINERALOGY." Economic Geology 117, no. 2 (March 1, 2022): 485–94. http://dx.doi.org/10.5382/econgeo.4915.
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Abstract Iron oxide copper-gold (IOCG) deposits form in spatial and genetic relation to hydrothermal iron oxide-alkali-calcic-hydrolytic alteration and thus show a mappable zonation of mineral assemblages toward the orebody. The mineral zonation of a breccia matrix-hosted orebody is efficiently mapped by regularly spaced samples analyzed by the scanning electron microscopy-integrated mineral analyzer technique. The method results in quantitative estimates of the mineralogy and allows the reliable recognition of characteristic alteration as well as mineralization-related mineral assemblages from detailed mineral maps. The Ernest Henry deposit is located in the Cloncurry district of Queensland and is one of Australia’s significant IOCG deposits. It is known for its association of K-feldspar altered clasts with iron oxides and chalcopyrite in the breccia matrix. Our mineral mapping approach shows that the hydrothermal alteration resulted in a characteristic zonation of minerals radiating outward from the pipe-shaped orebody. The mineral zonation is the result of a sequence of sodic alteration followed by potassic alteration, brecciation, and, finally, by hydrolytic (acid) alteration. The hydrolytic alteration primarily affected the breccia matrix and was related to economic mineralization. Alteration halos of individual minerals such as pyrite and apatite extend dozens to hundreds of meters beyond the limits of the orebody into the host rocks. Likewise, the Fe-Mg ratio in hydrothermal chlorites changes systematically with respect to their distance from the orebody. Geochemical data obtained from portable X-ray fluorescence (p-XRF) and petrophysical data acquired from a magnetic susceptibility meter and a gamma-ray spectrometer support the mineralogical data and help to accurately identify mineral halos in rocks surrounding the ore zone. Specifically, the combination of mineralogical data with multielement data such as P, Mn, As, P, and U obtained from p-XRF and positive U anomalies from radiometric measurements has potential to direct an exploration program toward higher Cu-Au grades.
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Talaei, Reza. "Landslide susceptibility zonation mapping using logistic regression and its validation in Hashtchin Region, northwest of Iran." Journal of the Geological Society of India 84, no. 1 (July 2014): 68–86. http://dx.doi.org/10.1007/s12594-014-0111-5.
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Prasanna Venkatesh, S., and S. E. Saranaathan. "IDENTIFICATION OF LANDSLIDE SUSCEPTIBILITY ZONATION IN CNG GHAT SECTION, GUDALUR, THE NILGIRIS – USING GIS BASED ANN/MULTI CRITERIA METHOD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 27, 2018): 871–75. http://dx.doi.org/10.5194/isprs-archives-xlii-5-871-2018.
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<p><strong>Abstract.</strong> Among the various natural hazards, landslide is the most widespread and damaging hazard. In recent times, throughout a lot of attention is being drawn to evaluate the risk due to landslides. The invention of remote sensing and GIS have been new vistas in the field of geo scientific studies viz. geomorphological mapping, groundwater potential mapping, disaster management etc. The present study has been undertaken to study different thematic maps like, contour, drainage, slope, aspect, curvature, DEM, DTM, drainage density, drainage intensity, geology, lineament, lineament density, lineament intensity, geomorphology, land use, weathering thickness, run off, soil thickness and buffer maps like road, drainage, lineament etc. in CNG ghat section, Gudalur, The Nilgiris. For this purpose, the satellite image IRS – RS2, LISS III January 2014 used to prepare different thematic maps. The contour, drainage and road network were incorporate from SoI Toposheets. The slope, curvature, aspects and buffer maps were prepared from GIS environments. Based on field studies, above said thematic maps (22 nos.) were prepared and were grouped into 3 categories viz. Geology, Hydrology and Terrain. In each category the input maps were assigned different score as well as each layer has been given different weightage. Finally the categories are analysed through multi – criteria analysis to find out 5 different vulnerability classes. The 5 different land susceptibility zones are classified as very low, low, moderate, high and very high. The percentages of area under different susceptibility classes are 3%, 20%, 51%, 25%, and 1% respectively. The locations of small area major landslides and slip locations were calculated from different years using (2010 and 2014) Trimble GPS in the field. The field data was converted into point layer in GIS and landslide inventory map was prepared. This map was superimposed in landslide susceptibility zonation map. As per field data 0%, 9.25%, 57.5%, 32% and 1.25% Slide points are come under very low, low, moderate, high, very high susceptibility zones respectively.</p>
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JEMMAH, A. I., and L. AIT BRAHIM. "Mass movement susceptibility mapping - A comparison of logistic regression and Weight of evidence methods in Taounate-Ain Aicha region (Central Rif, Morocco)." MATEC Web of Conferences 149 (2018): 02094. http://dx.doi.org/10.1051/matecconf/201814902094.
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Taounate region is known by a high density of mass movements which cause several human and economic losses. The goal of this paper is to assess the landslide susceptibility of Taounate using the Weight of Evidence method (WofE) and the Logistic Regression method (LR). Seven conditioning factors were used in this study: lithology, fault, drainage, slope, elevation, exposure and land use. Over the years, this site and its surroundings have experienced repeated landslides. For this reason, landslide susceptibility mapping is mandatory for risk prevention and land-use management. In this study, we have focused on recent large-scale mass movements. Finally, the ROC curves were established to evaluate the degree of fit of the model and to choose the best landslide susceptibility zonation. A total mass movements location were detected; 50% were randomly selected as input data for the entire process using the Spatial Data Model (SDM) and the remaining locations were used for validation purposes. The obtained WofE’s landslide susceptibility map shows that high to very high susceptibility zones contain 62% of the total of inventoried landslides, while the same zones contain only 47% of landslides in the map obtained by the LR method. This landslide susceptibility map obtained is a major contribution to various urban and regional development plans under the Taounate Region National Development Program.
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Pandey, Alok Kumar, and Avijit Mahala. "Landslide susceptibility zonation using analytical hierarchy process and GIS for the Nandakini River Valley, Central Himalaya." Journal of Scientific Research 66, no. 05 (2022): 12–15. http://dx.doi.org/10.37398/jsr.2022.660503.
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Nandakini River is a tributary of Ganga located in the central Himalayas of the Indian state of Uttarakhand. The area lies in the upper Ganga basin of the Himalayan rugged train. Landslide susceptibility mapping is of the foremost importance in an area like this one. It is required to alert the people. For the present study, eleven responsible causatives were considered namely drainage density, drainage, elevation, geology, geomorphology, groundwater depth, LULC, NDVI, rainfall, slope, and temperature. Analytical Hierarchy Process has been used to prioritize the factors. The degree of slope varies from 0 to 84. 54 where the high slope is in the northeastern and the low is in the northwestern part. Elevation varies from 827 meters to 6586 meters. Drainage density (0 to 4.67/sq.km), Rainfall (1224 to 1230 mm), Groundwater depth (41.79 to 42.29) etc. All the hydrological factors also accelerate landslide susceptibility. Rugged geomorphology with vulnerable geology helps the region to be landslide prone. The final landslide susceptible map indicates the area is highly vulnerable to landslides.
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Bousta, Mahfoud, and Lahsen Ait Brahim. "Weights of evidence method for landslide susceptibility mapping in Tangier, Morocco." MATEC Web of Conferences 149 (2018): 02042. http://dx.doi.org/10.1051/matecconf/201814902042.
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Tangier region is known by a high density of mass movements which cause several human and economic losses. The goal of this paper is to assess the landslide susceptibility of Tangier using the Weight of Evidence method (WofE). The method is founded on the principle that an event (landslide) is more likely to occur based on the relationship between the presence or absence of a predictive variable (predisposing factors) and the occurrence of this event. The inventory, description and analysis of mass movements were prepared. Then the main factors governing their occurrence (lithology, fault, slope, elevation, exposure, drainage and land use) were mapped before applying WofE. Finally, the ROC curves were established and the areas under curves (AUC) were calculated to evaluate the degree of fit of the model and to choose the best landslide susceptibility zonation. The prediction accuracy was found to be 70%. Obtained susceptibility map shows that 60% of inventoried landslides are in the high to very high susceptibility zones, which is very satisfactory for the validation of the adopted model and the obtained results. These zones are mainly located in the N-E and E part of the Tangier region in the soft and fragile facies of the marls and clays of the Tangier unit, where landuse is characterized by dominance of arable and agricultural land (lack of forest cover). From a purely spatial point of view, the localization of these two classes of susceptibility is completely corresponding to the ground truth data, that is to say that all the environmental and anthropogenic conditions are in place for making this area prone to landslide hazards. The obtained map is a decision-making tool for presenting, comparing and discussing development and urban scenarios in Tangier. These results fall within the context of sustainable development and will help to mitigate the socio-economic impacts usually observed when landslides are triggered.
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Kamp, Ulrich, Lewis A. Owen, Benjamin J. Growley, and Ghazanfar A. Khattak. "Back analysis of landslide susceptibility zonation mapping for the 2005 Kashmir earthquake: an assessment of the reliability of susceptibility zoning maps." Natural Hazards 54, no. 1 (September 22, 2009): 1–25. http://dx.doi.org/10.1007/s11069-009-9451-7.
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Shu, Heping, Zizheng Guo, Shi Qi, Danqing Song, Hamid Reza Pourghasemi, and Jiacheng Ma. "Integrating Landslide Typology with Weighted Frequency Ratio Model for Landslide Susceptibility Mapping: A Case Study from Lanzhou City of Northwestern China." Remote Sensing 13, no. 18 (September 10, 2021): 3623. http://dx.doi.org/10.3390/rs13183623.
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Although numerous models have been employed to address the issue of landslide susceptibility at regional scale, few have incorporated landslide typology into a model application. Thus, the aim of the present study is to perform landslide susceptibility zonation taking landslide classification into account using a data-driven model. The specific objective is to answer the question: how to select reasonable influencing factors for different types of landslides so that the accuracy of susceptibility assessment can be improved? The Qilihe District in Lanzhou City of northwestern China was undertaken as the test area, and a total of 12 influencing factors were set as the predictive variables. An inventory map containing 227 landslides was created first, which was divided into shallow landslides and debris flows based on the geological features, distribution, and formation mechanisms. A weighted frequency ratio model was proposed to calculate the landslide susceptibility. The weights of influencing factors were calculated by the integrated model of logistic regression and fuzzy analytical hierarchy process, whereas the rating among the classes within each factor was obtained by a frequency ratio algorithm. The landslide susceptibility index of each cell was subsequently calculated in GIS environment to create landslide susceptibility maps of different types of landslide. The analysis and assessment process were separately performed for each type of landslide, and the final landslide susceptibility map for the entire region was produced by combining them. The results showed that 73.3% of landslide pixels were classified into “very high” or “high” susceptibility zones, while “very low” or “low” susceptibility zones covered only 3.6% of landslide pixels. The accuracy of the model represented by receiver operating characteristic curve was satisfactory, with a success rate of 70.4%. When the landslide typology was not considered, the accuracy of resulted maps decreased by 1.5~5.4%.
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Sema, Hinotoli V., Balamurugan Guru, and Ramesh Veerappan. "Fuzzy gamma operator model for preparing landslide susceptibility zonation mapping in parts of Kohima Town, Nagaland, India." Modeling Earth Systems and Environment 3, no. 2 (May 17, 2017): 499–514. http://dx.doi.org/10.1007/s40808-017-0317-9.
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Milevski, Ivica, and Slavoljub Dragićević. "LANDSLIDES SUSCEPTIBILITY ZONATION OF THE TERRITORY OF NORTH MACEDONIA USING ANALYTICAL HIERARCHY PROCESS APPROACH." Contributions, Section of Natural, Mathematical and Biotechnical Sciences 40, no. 1 (June 10, 2019): 115. http://dx.doi.org/10.20903/csnmbs.masa.2019.40.1.136.
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Landslides are natural disasters that have an impact in many areas around the world including the territory of the Republic of Macedonia. In this country, about 300 large landslides are registered, most of which cause serious damage to the infrastructure almost every year. In that sense, the mapping of sites that are susceptible to landslides is essential for the management of these areas. This is a crucial step to prevent landslides in places where this could be expected or to mini-mize its damages. Therefore, a heuristic approach of Analytical Hierarchy Process (AHP) combined with Geographic In-formation System (GIS) and Remote Sensing (RS) is used in this work for the assessment of potential landslide areas in the Republic of Macedonia. In the procedure, 6 triggering factors indicating a strong influence on the landslide activity are selected, including lithology, slope angle, land cover, terrain curvature, distance from rivers and distance from roads. Through the procedure, expert-based weight of these factors is made. The LS model is produced with the summing up of the factor layers in the form of harmonized raster grids. Finally, the values of the grid model are classified according to the quantiles and natural breaks scheme. The produced maps show acceptable results confirmed by validation methods and ROC analysis, indicating that about 40% of the country area is under high and very high landslide susceptibility. This ap-proach can be further improved if combined with statistical methods in the form of a hybrid model.
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Marsala, Vincenzo, Alberto Galli, Giorgio Paglia, and Enrico Miccadei. "Landslide Susceptibility Assessment of Mauritius Island (Indian Ocean)." Geosciences 9, no. 12 (November 23, 2019): 493. http://dx.doi.org/10.3390/geosciences9120493.
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This work is focused on the landslide susceptibility assessment, applied to Mauritius Island. The study area is a volcanic island located in the western part of the Indian Ocean and it is characterized by a plateau-like morphology interrupted by three rugged mountain areas. The island is severely affected by geo-hydrological hazards, generally triggered by tropical storms and cyclones. The landslide susceptibility analysis was performed through an integrated approach based on morphometric analysis and preliminary Geographical Information System (GIS)-based techniques, supported by photogeological analysis and geomorphological field mapping. The analysis was completed following a mixed heuristic and statistical approach, integrated using GIS technology. This approach led to the identification of eight landslide controlling factors. Hence, each factor was evaluated by assigning appropriate expert-based weights and analyzed for the construction of thematic maps. Finally, all the collected data were mapped through a cartographic overlay process in order to realize a new zonation of landslide susceptibility. The resulting map was grouped into four landslide susceptibility classes: low, medium, high, and very high. This work provides a scientific basis that could be effectively applied in other tropical areas showing similar climatic and geomorphological features, in order to develop sustainable territorial planning, emergency management, and loss-reduction measures.
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Park, D. W., N. V. Nikhil, and S. R. Lee. "Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event." Natural Hazards and Earth System Sciences Discussions 1, no. 3 (June 5, 2013): 2547–87. http://dx.doi.org/10.5194/nhessd-1-2547-2013.
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Abstract. This paper presents the results from application of a regional, physically-based stability model: Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) for a catchment on Woomyeon Mountain, Seoul, Korea. This model couples an infinite-slope stability analysis with a one-dimensional analytical solution to predict the transient pore pressure response to the infiltration of rainfall. TRIGRS also adopts the Geographic Information Systems (GIS) framework for determining the whole behaviour of a slope. In this paper, we suggest an index for evaluating the results produced by the model. Particular attention is devoted to the prediction of routes of debris flow, using a runoff module. In this context, the paper compares observed landslide and debris flow events with those predicted by the TRIGRS model. The TRIGRS model, originally developed to predict shallow landslides, has been extended in this study for application to debris flows. The results predicted by the TRIGRS model are presented as safety factor (FS) maps corresponding to transient rainfall events, and in terms of debris flow paths using methods proposed by several researchers in hydrology. In order to quantify the accuracy of the model, we proposed an index called LRclass (landslide ratio for each predicted FS class). The LRclass index is mainly applied in regions where the landslide scar area is not well defined (or is unknown), in order to avoid over-estimation of the model results. The use of the TRIGRS routing module was proposed to predict the paths of debris flow, especially in areas where the rheological properties and erosion rates of the materials are difficult to obtain. Although an improvement in accuracy is needed, this module is very useful for preliminary spatiotemporal assessment over wide areas. In summary, the TRIGRS model is a powerful tool of use to decision makers for susceptibility mapping, particularly when linked with various advanced applications using GIS spatial functions.
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Park, D. W., N. V. Nikhil, and S. R. Lee. "Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event." Natural Hazards and Earth System Sciences 13, no. 11 (November 14, 2013): 2833–49. http://dx.doi.org/10.5194/nhess-13-2833-2013.
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Abstract. This paper presents the results from the application of a regional, physically based stability model: Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) for a region on Woomyeon Mountain, Seoul, South Korea. This model couples an infinite-slope stability analysis with a one-dimensional analytical solution to predict the transient pore pressure response to the infiltration of rainfall. TRIGRS also adopts the geographic information system (GIS) framework for determining the whole behaviour of a slope. In this paper, we suggest an index for evaluating the results produced by the model. Particular attention is devoted to the prediction of routes of debris flow, using a runoff module. In this context, the paper compares observed landslide and debris flow events with those predicted by the TRIGRS model. The TRIGRS model, originally developed to predict shallow landslides, has been extended in this study for application to debris flows. The results predicted by the TRIGRS model are presented as safety factor (FS) maps corresponding to transient rainfall events, and in terms of debris flow paths using methods proposed by several researchers in hydrology. In order to quantify the effectiveness of the model, we proposed an index called LRclass (landslide ratio for each predicted FS class). The LRclass index is mainly applied in regions where the landslide scar area is not well defined (or is unknown), in order to avoid overestimation of the model results. The use of the TRIGRS routing module was proposed to predict the paths of debris flow, especially in areas where the rheological properties and erosion rates of the materials are difficult to obtain. Although an improvement in accuracy is needed, this module is very useful for preliminary spatio-temporal assessment over wide areas. In summary, the TRIGRS model is a powerful tool of use to decision makers for susceptibility mapping, particularly when linked with various advanced applications using GIS spatial functions.
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Berhane, Gebremedhin, and Kumarra Tadesse. "Landslide susceptibility zonation mapping using statistical index and landslide susceptibility analysis methods: A case study from Gindeberet district, Oromia Regional State, Central Ethiopia." Journal of African Earth Sciences 180 (August 2021): 104240. http://dx.doi.org/10.1016/j.jafrearsci.2021.104240.
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Rouhani, Hamed, Aboalhasan Fathabadi, and Jantiene Baartman. "A wrapper feature selection approach for efficient modelling of gully erosion susceptibility mapping." Progress in Physical Geography: Earth and Environment 45, no. 4 (January 20, 2021): 580–99. http://dx.doi.org/10.1177/0309133320979897.
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Identifying the vulnerability level of an area to soil erosion, particularly gully erosion, is key to the development of an efficient management strategy for policymakers. While efforts into susceptibility mapping of natural disasters have grown in recent years, understanding the most relevant predictive causal factors is still a challenge. As the selection of these factors, among many potentially relevant factors, is an important part of the model selection process, we propose a hybrid intelligent approach for the optimal selection of a set of relevant factors based on logistic regression (LR) and genetic algorithms. In order to verify the effectiveness of the proposed approach, this study also identified areas that were highly susceptible to gully erosion using three different classifiers – namely, the LR, support vector machine (SVM) and k-nearest neighbours (k-NN) techniques. We tested the approach in the Yeli Bedrag watershed in north-eastern Golestan province, Iran. The results showed that the elevation, distance to fault, slope and the index of connectivity were the most important causal factors affecting the successful prediction of gully occurrence. Comparison of maximum True Skill Statistic values showed that increased model sophistication did not necessarily result in a higher level of model performance. In terms of performance, k-NN was superior to the SVM and LR methods. This method can be effectively used for gully erosion susceptibility (GES) zonation in the study area, which is very important to support spatial planning to initiate designing mitigation strategies and conservation needs over a large area, or to plan additional conservation efforts and relocate soil conservation plans. In conclusion, our findings indicate that by incorporating the proposed hybrid intelligent approach, the number of relevant factors for GES mapping was reduced, while classification accuracy was increased.
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Tadele, Tamene. "Landslide Hazard Assessment and Zonation by using Slope Susceptibility Evaluation Parameter (SSEP) Rating Scheme- a Case from Debre Sina, Northern Ethiopia." East African Journal of Biophysical and Computational Sciences 3, no. 1 (February 5, 2022): 23–42. http://dx.doi.org/10.4314/eajbcs.v3i1.4s.
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Rainfall-induced landslides of different types and sizes frequently affect the hilly and mountainous terrains of the highlands of Ethiopia. The principal objective of the proposed research study was intended to prepare a landslide hazard zonation map of the area, particularly for hazardous zones. In the present study, the Slope Susceptibility Evaluation Parameter rating scheme has been implemented as a relevant approach to map the landslide hazard of the Debresina area, which has experienced slope failure problems for a long period of time. The geology of the area includes quaternary sediments, ignimbrite, rhyolite, different kinds of basalts, and tuff deposits, which are highly weathered and changed into unconsolidated sediments at some localities. Locally observed geological structures such as joints, dykes, and other discontinuities have a considerable role in the initiation of landslide hazards. As a general methodology, a facet map was prepared from a topographic map (1:50,000) and rating values were assigned to each causative parameter (both intrinsic and external) based on its severity in triggering landslide hazard. The study area was classified into three hazard classes, of which 25 % of the slopes fall into a moderate hazard zone, while 58 % and 17 %were found to be high and very high hazard zones, respectively. Validation of the landslide hazard zonation map with past landslide activities suggests the rationality of the considered governing parameters, the adopted technique, tools, and procedures in developing the study area's landslide hazard map. Further, in order to validate the landslide hazard map prepared during the present study, active landslide activities and potential instability areas, delineated through inventory mapping, were overlaid on it, which yielded promising results.
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Balamurugan, Guru, Veerappan Ramesh, and Mangminlen Touthang. "Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India." Natural Hazards 84, no. 1 (June 28, 2016): 465–88. http://dx.doi.org/10.1007/s11069-016-2434-6.
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Subiaya, Bashir, and T. Ramkumar. "A multi-temporal landslide inventory and hazard zonation using relative effect method along the Mughal road Shopian, India." Disaster Advances 14, no. 7 (June 25, 2021): 42–51. http://dx.doi.org/10.25303/147da4221.
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Landslide inventory and thematic data are of utmost importance in the domain of landslide hazard mapping. The union territory of Jammu and Kashmir, India surrounded by the Himalayan and the Pir-Panjal mountain range is prone to landslides and has already caused havoc at many places. The present study aims to provide the landslide inventory of the Mughal Road, Shopian, which lies in the Pir Panjal range of Kashmir valley. Multidate satellite data of the years 2008 to 2020 are utilized to create an inventory of landslides in this area.The use of high-resolution satellite imagery made it possible to delineate the shallow as well as the deep landslides along the roadside where they occur frequently. To understand the landslide causes, a statistical technique, relative effect method has been implemented in this study. This method helped in mapping the hazard zone areas. The relative effect of each causative factor on landslides is determined by calculating the ratio of coverage and slide which were analyzed in GIS environment. The resulting landslide hazard zone map has been classified as very low, low, moderate, high and very high zones. Out of the total area, 12.62% is critical to landslides, 21.45% is highly prone and 24.84% is moderately prone while 21.94% is low and 19.13% is very low prone to landslides. The outcome of this susceptibility modeling will be beneficial for handling and monitoring the forthcoming landslides as well as the fortification of the general public and environmental hazards of the study area. It will also help the planners in the development around the study area.
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Mirabedini, M. E., E. Haghshenas, and N. Ganjian. "Estimating the Geological Strength Index (GSI) in Regional Seismic-Landslide Zonation Using the Empirical Regression Model." Advances in Civil Engineering 2022 (June 25, 2022): 1–14. http://dx.doi.org/10.1155/2022/4798523.
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The assessment of the strength parameters of geological formations in regional scale which encounters thousands of slopes is a complicated approach and time consuming and needs huge field work. This issue is an important research topic concerning the regional seismic-landslide susceptibility analysis or hazard zonation. An empirical regression model was presented to estimate the Geological Strength Index (GSI) with an implication on geological quadrangle of Gorgan region at Alborz mountains range (north of Iran). Two main sets of data were applied in this study: (1) geomorphological data including the slope height, aspect, and distance from faults and distance from thrusts and (2) the physical and mechanical properties of rocks including the unit weight, uniaxial compressive strength (σci), and the petrographic constant (mί) of intact rock. The first group was extracted from a 1 : 100,000 digital geologic map and 10 m digital elevation model (DEM) and the second group was obtained from the Hoek–Brown failure criterion recommended tables. Linear regression equations were generated applying data collected from 294 studied stations using SPSS software. The regression equation predicted GSI in terms of (1) the distance from faults, (2) the distance from thrusts, and (3) the uniaxial compressive strength (σci). The equation had an R2 value of 0.739 and thus fit well to the data. The new method in its present state was recommended for the estimation of the GSI values in regional scale conditions for the assessment of landslide susceptibility and hazard mapping or post events landslide occurrence prediction in the case of probable big earthquakes in Alborz area that is required for emergency responses. The results indicated that the estimation error was about ±30 while the average error was within +5 and −5 and average error percentage was about 3%.
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Guru, Balamurugan, Ramesh Veerappan, Francis Sangma, and Somnath Bera. "Comparison of probabilistic and expert-based models in landslide susceptibility zonation mapping in part of Nilgiri District, Tamil Nadu, India." Spatial Information Research 25, no. 6 (October 16, 2017): 757–68. http://dx.doi.org/10.1007/s41324-017-0143-1.
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Manchar, Nabil, Chaouki Benabbas, Riheb Hadji, Foued Bouaicha, and Florina Grecu. "Landslide Susceptibility Assessment in Constantine Region (NE Algeria) By Means of Statistical Models." Studia Geotechnica et Mechanica 40, no. 3 (December 21, 2018): 208–19. http://dx.doi.org/10.2478/sgem-2018-0024.
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AbstractThe purpose of the present study was to compare the prediction performances of three statistical methods, namely, information value (IV), weight of evidence (WoE) and frequency ratio (FR), for landslide susceptibility mapping (LSM) at the east of Constantine region. A detailed landslide inventory of the study area with a total of 81 landslide locations was compiled from aerial photographs, satellite images and field surveys. This landslide inventory was randomly split into a testing dataset (70%) for training the models, and the remaining (30%) was used for validation purpose. Nine landslide-related factors such as slope gradient, slope aspect, elevation, distance to streams, lithology, distance to lineaments, precipitation, Normalized Difference Vegetation Index (NDVI) and stream density were used in the landslide susceptibility analyses. The inventory was adopted to analyse the spatial relationship between these landslide factors and landslide occurrences. Based on IV, WoE and FR approaches, three landslide susceptibility zonation maps were categorized, namely, “very high, high, moderate, low, and very low”. The results were compared and validated by computing area under Road the receiver operating characteristic (ROC) curve (AUC). From the statistics, it is noted that prediction scores of the FR, IV and WoE models are relatively similar with 73.32%, 73.95% and 79.07%, respectively. However, the map, obtained using the WoE technique, was experienced to be more suitable for the study area. Based on the results, the produced LSM can serve as a reference for planning and decision-making regarding the general use of the land.
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Horton, P., M. Jaboyedoff, B. Rudaz, and M. Zimmermann. "Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale." Natural Hazards and Earth System Sciences 13, no. 4 (April 9, 2013): 869–85. http://dx.doi.org/10.5194/nhess-13-869-2013.
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Abstract. The development of susceptibility maps for debris flows is of primary importance due to population pressure in hazardous zones. However, hazard assessment by process-based modelling at a regional scale is difficult due to the complex nature of the phenomenon, the variability of local controlling factors, and the uncertainty in modelling parameters. A regional assessment must consider a simplified approach that is not highly parameter dependant and that can provide zonation with minimum data requirements. A distributed empirical model has thus been developed for regional susceptibility assessments using essentially a digital elevation model (DEM). The model is called Flow-R for Flow path assessment of gravitational hazards at a Regional scale (available free of charge under http://www.flow-r.org) and has been successfully applied to different case studies in various countries with variable data quality. It provides a substantial basis for a preliminary susceptibility assessment at a regional scale. The model was also found relevant to assess other natural hazards such as rockfall, snow avalanches and floods. The model allows for automatic source area delineation, given user criteria, and for the assessment of the propagation extent based on various spreading algorithms and simple frictional laws. We developed a new spreading algorithm, an improved version of Holmgren's direction algorithm, that is less sensitive to small variations of the DEM and that is avoiding over-channelization, and so produces more realistic extents. The choices of the datasets and the algorithms are open to the user, which makes it compliant for various applications and dataset availability. Amongst the possible datasets, the DEM is the only one that is really needed for both the source area delineation and the propagation assessment; its quality is of major importance for the results accuracy. We consider a 10 m DEM resolution as a good compromise between processing time and quality of results. However, valuable results have still been obtained on the basis of lower quality DEMs with 25 m resolution.
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Lee, C. F., W. K. Huang, C. L. Chiu, and C. C. Chi. "INVENTORY, MAPPING, GEOMORPHIC CHARACTERIZATION, AND VALIDATION OF DEEP-SETATED LANDSLIDES USING SKY-VIEW FACTOR VISUALIZATION: NORTHERN, CENTRAL, AND SOUTHERN TAIWAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W4 (March 6, 2018): 319–25. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w4-319-2018.
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<p><strong>Abstract.</strong> Extreme rainfall with long-term period plays a principal role in triggering deep-seated landslide around the mountainous area. A well-known typhoon Morakot, the most destructive event occurred in August 2009, battered southern Taiwan and caused severe casualties in Siaolin Village. To reduce the damage and to prevent loss of life resulting from the catastrophic landslide, this study adopted high-resolution topographic data which extracted from airborne LiDAR scanning to interpret both recent and ancient deep-seated landslides in northern, central, and southern Taiwan. Firstly, a relief visualization technique called sky-view factor was utilized to generate the quasi-3D map by overlapping slope gradient, and multiple direction hillshading maps, allowing one to interpret manually detailed landslide topography and assess the hazard potential. The study area of the on-going project covers an area of 17,000&thinsp;km<sup>2</sup>. This study recognized main scarp and landslide body in polygon pattern by landslide micro-topography interpretation; it showed more than 700 deep-seated landslides were mapped and located on Central Range and Western foothills in Taiwan. The spatial distribution of deep-seated landslide relates highly to the regional strike of formation, daylight at the toe, river-bank erosion, and drainage density. Additionally, the detrimental geomorphic and topographic features are extracted to evaluate the landslide activity in the future. For a landslide zonation which characterized with sharp scarp and greater deformation rate, it usually may represent higher failure susceptibility. This work also uses the 3-D terrain model created by drone photography and geomorphometric analysis to validate the expert-based landslide susceptibility. Furthermore, the result of the study will contribute updating the national-wide environmental geologic map and provide competent authority to make decisions reducing the geohazard risk.</p>
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Sharma, Mukta, Ritambhara K. Upadhyay, Gaurav Tripathi, Naval Kishore, Achala Shakya, Gowhar Meraj, Shruti Kanga, et al. "Assessing Landslide Susceptibility along India’s National Highway 58: A Comprehensive Approach Integrating Remote Sensing, GIS, and Logistic Regression Analysis." Conservation 3, no. 3 (September 7, 2023): 444–59. http://dx.doi.org/10.3390/conservation3030030.
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The NH 58 area in India has been experiencing an increase in landslide occurrences, posing significant threats to local communities, infrastructure, and the environment. The growing need to identify areas prone to landslides for effective disaster risk management, land use planning, and infrastructure development has led to the increased adoption of advanced geospatial technologies and statistical methods. In this context, this research article presents an in-depth analysis aimed at developing a landslide susceptibility zonation (LSZ) map for the NH 58 area using remote sensing, GIS, and logistic regression analysis. The study incorporates multiple geo-environmental factors for analysis, such as slope aspect, curvature, drainage density, elevation, fault distance, flow accumulation, geology, geomorphology, land use land cover (LULC), road distance, and slope angle. Utilizing 50% of the landslide inventory data, the logistic regression model was trained to determine correlations between causal factors and landslide occurrences. The logistic regression model was then employed to calculate landslide probabilities for each mapping unit within the NH 58 area, which were subsequently classified into relative susceptibility zones using a statistical class break technique. The model’s accuracy was verified through ROC curve analysis, resulting in a 92% accuracy rate. The LSZ map highlights areas near road cut slopes as highly susceptible to landslides, providing crucial information for land use planning and management to reduce landslide risk in the NH 58 area. The study’s findings are beneficial for policymakers, planners, and other stakeholders involved in regional disaster risk management. This research offers a comprehensive analysis of landslide-influencing factors in the NH 58 area and introduces an LSZ map as a valuable tool for managing and mitigating landslide risks. The map also serves as a critical reference for future research and contributes to the broader understanding of landslide susceptibility in the region.