scholarly journals The Application of the Hybrid GIS Spatial Multi-Criteria Decision Analysis Best–Worst Methodology for Landslide Susceptibility Mapping

2019 ◽  
Vol 8 (2) ◽  
pp. 79 ◽  
Author(s):  
Ljubomir Gigović ◽  
Siniša Drobnjak ◽  
Dragan Pamučar
Keyword(s):  
Decision Analysis ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Geographical Information ◽  
Weighted Averaging ◽  
Landslide Susceptibility Mapping ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Multi Criteria Decision Analysis ◽  
Aggregation Methods

The main goal of this article is to produce a landslide susceptibility map by using the hybrid Geographical Information System (GIS) spatial multi-criteria decision analysis best–worst methodology (MCDA-BWM) in the western part of the Republic of Serbia. Initially, a landslide inventory map was prepared using the National Landslide Database, aerial photographs, and also by carrying out field surveys. A total of 1082 landslide locations were detected. This methodology considers the fifteen conditioning factors that are relevant to landslide susceptibility mapping: the elevation, slope, aspect, distance to the road network, distance to the river, distance to faults, lithology, the Normalized Difference Vegetation Index (NDVI), the Topographic Wetness Index (TWI), the Stream Power Index (SPI), the Sediment Transport Index (STI), annual rainfall, the distance to urban areas, and the land use/cover. The expert evaluation takes into account the nature and severity of the observed criteria, and it was tested by using two scenarios: the different aggregation methods of the BWM. The prediction performances of the generated maps were checked by the receiver operating characteristics (ROCs). The validation results confirmed that the areas under the ROC curve for the weighted linear combination (WLC) and the ordered weighted averaging (OWA) aggregation methods of the MCDA-BWM have a very high accuracy. The results of the landslide susceptibility assessment obtained by applying the proposed best–worst method were the first step in the development of landslide risk management and they are expected to be used by local governments for effective management planning purposes.

scholarly journals GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia

2019 ◽  
Vol 11 (1) ◽  
pp. 708-726
Author(s):  
Zorgati Anis ◽  
Gallala Wissem ◽  
Vakhshoori Vali ◽  
Habib Smida ◽  
Gaied Mohamed Essghaier
Keyword(s):  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Satellite Image ◽  
Susceptibility Mapping ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
North Western ◽  
Landslide Susceptibility Maps

AbstractThe Tunisian North-western region, especially Tabarka and Ain-Drahim villages, presents many landslides every year. Therefore, the landslide susceptibility mapping is essential to frame zones with high landslide susceptibility, to avoid loss of lives and properties. In this study, two bivariate statistical models: the evidential belief functions (EBF) and the weight of evidence (WoE), were used to produce landslide susceptibility maps for the study area. For this, a landslide inventory map was mapped using aerial photo, satellite image and extensive field survey. A total of 451 landslides were randomly separated into two datasets: 316 landslides (70%) for modelling and 135 landslides (30%) for validation. Then, 11 landslide conditioning factors: elevation, slope, aspect, lithology, rainfall, normalized difference vegetation index (NDVI), land cover/use, plan curvature, profile curvature, distance to faults and distance to drainage networks, were considered for modelling. The EBF and WoE models were well validated using the Area Under the Receiver Operating Characteristic (AUROC) curve with a success rate of 87.9% and 89.5%, respectively, and a predictive rate of 84.8% and 86.5%, respectively. The landslide susceptibility maps were very similar by the two models, but the WoE model is more efficient and it can be useful in future planning for the current study area.

scholarly journals National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making

2020 ◽  
Vol 9 (6) ◽  
pp. 393 ◽  
Author(s):  
Meisam Moharrami ◽  
Amin Naboureh ◽  
Thimmaiah Gudiyangada Nachappa ◽  
Omid Ghorbanzadeh ◽  
Xudong Guan ◽  
...  
Keyword(s):  
Decision Making ◽  
Landslide Susceptibility ◽  
National Level ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Pairwise Comparisons ◽  
Multi Criteria Decision Making ◽  
Mountainous Regions ◽  
Wide Range

Landslides are one of the most detrimental geological disasters that intimidate human lives along with severe damages to infrastructures and they mostly occur in the mountainous regions across the globe. Landslide susceptibility mapping (LSM) serves as a key step in assessing potential areas that are prone to landslides and could have an impact on decreasing the possible damages. The application of the fuzzy best-worst multi-criteria decision-making (FBWM) method was applied for LSM in Austria. Further, the role of employing a few numbers of pairwise comparisons on LSM was investigated by comparing the FBWM and Fuzzy Analytical Hierarchical Process (FAHP). For this study, a wide range of data was sourced from the Geological Survey of Austria, the Austrian Land Information System, Humanitarian OpenStreetMap Team, and remotely sensed data were collected. We used nine conditioning factors that were based on the previous studies and geomorphological characteristics of Austria, such as elevation, slope, slope aspect, lithology, rainfall, land cover, distance to drainage, distance to roads, and distance to faults. Based on the evaluation of experts, the slope conditioning factor was chosen as the best criterion (highest impact on LSM) and the distance to roads was considered as the worst criterion (lowest impact on LSM). LSM was generated for the region based on the best and worst criterion. The findings show the robustness of FBWM in landslide susceptibility mapping. Additionally, using fewer pairwise comparisons revealed that the FBWM can obtain higher accuracy as compared to FAHP. The finding of this research can help authorities and decision-makers to provide effective strategies and plans for landslide prevention and mitigation at the national level.

scholarly journals Evaluating underlying causative factors for earthquake-induced landslides and landslide susceptibility mapping in Upper Indrawati Watershed, Nepal

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pawan Gautam ◽  
Tetsuya Kubota ◽  
Aril Aditian
Keyword(s):  
Land Cover ◽  
Landslide Susceptibility ◽  
Total Curvature ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Area Index ◽  
Causal Factors ◽  
Causative Factors

AbstractThe main objective of this study is to understand the overall impact of earthquake in upper Indrawati Watershed, located in the high mountainous region of Nepal. Hence, we have assessed the relationship between the co-seismic landslide and underlying causative factors as well as performed landslide susceptibility mapping (LSM) to identify the landslide susceptible zone in the study area. We assessed the landslides distribution in terms of density, number, and area within 85 classes of 13 causal factors including slope, aspect, elevation, formation, land cover, distance to road and river, soil type, total curvature, seismic intensity, topographic wetness index, distance to fault, and flow accumulation. The earthquake-induced landslide is clustered in Northern region of the study area, which is dominated by steep rocky slope, forested land, and low human density. Among the causal factors, 'slope' showed positive correlation for landslide occurrence. Increase in slope in the study area also escalates the landslide distribution, with highest density at 43%, landslide number at 4.34/km2, and landslide area abundance at 2.97% in a slope class (> 50°). We used logistic regression (LR) for LSM integrating with geographic information system. LR analysis depicts that land cover is the best predictor followed by slope and distance to fault with higher positive coefficient values. LSM was validated by assessing the correctly classified landslides under susceptibility categories using area under curve (AUC) and seed cell area index (SCAI). The LSM approach showed good accuracy with respective AUC values for success rate and prediction rate of 0.843 and 0.832. Similarly, the decreasing SCAI value from very low to very high susceptibility categories advise satisfactory accuracy of the LSM approach.

scholarly journals Landslide Susceptibility Region Mapping Using GIS, Analytic Hierarchy Process Model, and Multi-Criteria Analysis at the Chemoga Watershed, Upper Blue Nile, Ethiopia

2021 ◽  
Author(s):  
Hunegnaw Desalegn ◽  
Arega Mulu ◽  
Banchiamlak Damtew
Keyword(s):  
Decision Analysis ◽  
Landslide Susceptibility ◽  
Process Model ◽  
Google Earth ◽  
Slope Aspect ◽  
Human Beings ◽  
Multi Criteria Decision Analysis ◽  
Susceptibility Maps ◽  
Hierarchy Process ◽  
Landslide Susceptibility Maps

Abstract Landslide susceptibility consists of an essential component in the day-to-day activity of human beings. A landslide incident typically happening at a low rate of recurrence when compared and in contrast to other events. This might be generated into main natural catastrophes relating to widespread and undesirable sound effects. Therefore, based on this perception and merging with the expert approach that has been the propensity to encourage and defy extreme physical development to generate a methodology to use GIS, AHP, and Multi-criteria decision analysis for landslide susceptibility maps. A geographic information system is a grouping with additional methods, such as the method for multi-criteria decision making. MCDA techniques are applied under such circumstances to categorize and class decisions for successive comprehensive estimation or else to state possible from impossible potentiality with various landslides. Analytical Hierarchy Process (AHP) constructively applies for conveying influence to different criteria within Multi-criteria decision analysis. The causative landslide weights utilized within this research were elevation, slope, aspect, Soil type, Lithology, Distance to stream, Land use land cover, rainfall and drainage density achieved from various sources. Subsequently, to explain the significance of each constraint into landslide susceptibility weights of all factors were finding out AHP technique. Generally, landslide susceptibility maps of all factors were multiplied to their weights to acquire with the AHP technique. The result showed that the AHP methods are comparatively good quality estimators of landslide susceptibility identification within the chemoga watershed. As the result, the Chemoga watershed area of landslide susceptibility map classes was classified as 46.52%, 13.83%.18.71%, 15.39%, and 5.55% of the occurred landslide fall to very low, low, moderate, high, and very high susceptibility zones respectively. Performance and accuracy of modeled maps have been established using GPS field data and Google earth data landslide map and Area under Curve (AUC) of the Receiver Operating Characteristic curve (ROC). The research outcomes inveterate the very good test consistency of the generated maps. As the result, validation depends on the ROC specifies the accuracy of the map formed with the AHP merged through weighted overly method illustrated very good accuracy of AUC value 81.45%.

scholarly journals Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 325 ◽  
Author(s):  
Guirong Wang ◽  
Xinxiang Lei ◽  
Wei Chen ◽  
Himan Shahabi ◽  
Ataollah Shirzadi
Keyword(s):  
Landslide Susceptibility ◽  
Radial Basis Function Network ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

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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