scholarly journals A Comparative Study of Statistics-Based Landslide Susceptibility Models: A Case Study of the Region Affected by the Gorkha Earthquake in Nepal

2019 ◽  
Vol 8 (2) ◽  
pp. 94 ◽  
Author(s):  
Sansar Meena ◽  
Omid Ghorbanzadeh ◽  
Thomas Blaschke
Keyword(s):  
Statistical Models ◽  
Landslide Susceptibility ◽  
Area Under The Curve ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Operating Characteristics ◽  
Gorkha Earthquake ◽  
Conditioning Factors ◽  
Multi Criteria Evaluation

As a result of the Gorkha earthquake in 2015, about 9000 people lost their lives and many more were injured. Most of these losses were caused by earthquake-induced landslides. Sustainable planning and decision-making are required to reduce the losses caused by earthquakes and related hazards. The use of remote sensing and geographic information systems (GIS) for landslide susceptibility mapping can help planning authorities to prepare for and mitigate the consequences of future hazards. In this study, we developed landslide susceptibility maps using GIS-based statistical models at the regional level in central Nepal. Our study area included the districts affected by landslides after the Gorkha earthquake and its aftershocks. We used the 23,439 landslide locations obtained from high-resolution satellite imagery to evaluate the differences in landslide susceptibility using analytical hierarchy process (AHP), frequency ratio (FR) and hybrid spatial multi-criteria evaluation (SMCE) models. The nine landslide conditioning factors of lithology, land cover, precipitation, slope, aspect, elevation, distance to roads, distance to drainage and distance to faults were used as the input data for the applied landslide susceptibility mapping (LSM) models. The spatial correlation of landslides and these factors were identified using GIS-based statistical models. We divided the inventory into data used for training the statistical models (70%) and data used for validation (30%). Receiver operating characteristics (ROC) and the relative landslide density index (R-index) were used to validate the results. The area under the curve (AUC) values obtained from the ROC approach for AHP, FR and hybrid SMCE were 0.902, 0.905 and 0.91, respectively. The index of relative landslide density, R-index, values in sample datasets of AHP, FR and hybrid SMCE maps were 53%, 58% and 59% for the very high hazard classes. The final susceptibility results will be beneficial for regional planning and sustainable hazard mitigation.

scholarly journals Landslide Susceptibility Mapping using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh

2021 ◽  
Author(s):  
Md. Sharafat Chowdhury ◽  
Bibi Hafsa
Keyword(s):  
Statistical Models ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Information Value ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Conditioning Factors

Abstract This study attempts to produce Landslide Susceptibility Map for Chattagram District of Bangladesh by using five GIS based bivariate statistical models, namely the Frequency Ratio (FR), Shanon’s Entropy (SE), Weight of Evidence (WofE), Information Value (IV) and Certainty Factor (CF). A secondary landslide inventory database was used to correlate the previous landslides with the landslide conditioning factors. Sixteen landslide conditioning factors of Slope Aspect, Slope Angle, Geology, Elevation, Plan Curvature, Profile Curvature, General Curvature, Topographic Wetness Index, Stream Power Index, Sediment Transport Index, Topographic Roughness Index, Distance to Stream, Distance to Anticline, Distance to Fault, Distance to Road and NDVI were used. The Area Under Curve (AUC) was used for validation of the LSMs. The predictive rate of AUC for FR, SE, WofE, IV and CF were 76.11%, 70.11%, 78.93%, 76.57% and 80.43% respectively. CF model indicates 15.04% of areas are highly susceptible to landslide. All the models showed that the high elevated areas are more susceptible to landslide where the low-lying river basin areas have a low probability of landslide occurrence. The findings of this research will contribute to land use planning, management and hazard mitigation of the CHT region.

scholarly journals A Hybrid Spatial Multi-Criteria Evaluation Method for Mapping Landslide Susceptible Areas in Kullu Valley, Himalayas

Geosciences ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 156 ◽  
Author(s):  
Sansar Meena ◽  
Brijendra Mishra ◽  
Sepideh Tavakkoli Piralilou
Keyword(s):  
Landslide Susceptibility ◽  
Evaluation Method ◽  
Integration Method ◽  
Risk Mitigation ◽  
Area Under The Curve ◽  
Development Planning ◽  
Landslide Susceptibility Mapping ◽  
Conditioning Factors ◽  
Multi Criteria Evaluation ◽  
Kullu Valley

In this paper we report our results from analysing a hybrid spatial multi-criteria evaluation (SMCE) method for generating landslide susceptibility mapping (LSM). This study is the first of its kind in the Kullu valley, Himalayas. We used eight related geospatial conditioning factors from three main groups: geological, morphological and topographical factors. Our landslide inventory dataset has a total of 149 GPS points of landslide locations, collected based on a field survey in July 2018. The relationships between landslide locations and conditioning factors were determined using the GIS-based statistical methods of frequency ratio (FR), multi-criteria decision-making (MCDM) and the integration method of hybrid SMCE. We compared the performance of applied methods by dividing the inventory into testing (70%) and validation (30%) datasets. The area under the curve (AUC) was used to validate the results. The integration method of hybrid SMCE gave the highest accuracy rate (0.910) compared to the other two methods, with 0.797 and 0.907 accuracy rates for the analytical hierarchy process (AHP) and FR, respectively. The applied methodologies are easily transferable to other areas, and the resulting landslide susceptibility maps (LSMs) can be useful for risk mitigation and development planning purposes in the Kullu valley, Himalayas.

scholarly journals Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1848
Author(s):  
Chenglong Yu ◽  
Jianping Chen
Keyword(s):  
Landslide Susceptibility ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Susceptibility Mapping ◽  
Mitigation Strategies ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Suitable Model ◽  
Mapping Unit ◽  
Slope Unit

Landslides are one of the most extensive geological disasters in the world. The objective of this study was to assess the performances of different landslide susceptibility models information content method (ICM), analytical hierarchy process (AHP), and random forest (RF) model) and mapping unit (slope unit and grid unit) for landslide susceptibility mapping in the Helong city, Jilin province, northeastern China. First, a total of 159 landslides were mapped in the study area based on a geological hazard survey (1:50,000) of Helong city. Then, the slope units of the study area were divided by using the curvature watershed method. Next, eight influencing factors, namely, lithology, slope angle, slope aspect, rainfall, land use, seismic intensity, distance to river, and distance to fault, were selected to map the landslide susceptibility based on geological data, field survey, and landslide information. Afterward, landslide susceptibility modeling of landslide inventory data is performed for extracting and learning the symmetry latent in data patterns and relationships by three landslide susceptibility models and utilizing it to predict landslide susceptibility. Finally, the receiver operating characteristic (ROC) curve was used to compare the landslide susceptibility models. In addition, results based on grid units were calculated for comparison. The AUC (the area under the curve) result for ICM, AHP, and RF model was 87.1%, 80.5%, and 94.6% for slope units, and 83.4%, 70.9%, and 91.3% for grid units, respectively. Based on the overall assessments, the SU-RF model was the most suitable model for landslide susceptibility mapping. Consequently, these methods can be very useful for landslide hazard mitigation strategies.

scholarly journals Landslide susceptibility mapping using Spatial Multi-Criteria Evaluation (SMCE) method in Camba Sub-district, Maros Regency, South Sulawesi

2020 ◽  
Vol 153 ◽  
pp. 02007
Author(s):  
Nahra Oktaviani ◽  
Yoanna Ristya ◽  
Muhammad Fadhil ◽  
Eko Kusratmoko
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Disaster Mitigation ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Physical Factors ◽  
Preparedness Planning ◽  
South Sulawesi ◽  
Multi Criteria Evaluation ◽  
Very High

This research presents the results of a landslide susceptibility mapping using Geographic Information Systems (GIS) based statistical namely Spatial Multi-Criteria Evaluation (SMCE) in Camba Sub-district, Maros Regency, South Sulawesi. Ten physical factors encompassed soil type, slope, slope aspect, rock type, altitude, land cover, distance from the river, rainfall, distance from faults, and distance from the road that collected from several sources and used to determined landslide susceptible areas. SMCE was applied to classify the degree of landslide susceptibility from low to very high classes. Validation using 30 points of landslide events obtained from field survey. The result showed an area with high and very high classes has an area 2079 ha (18,3 %) and 52,5 ha (0,46 %) distributed in the southern region. The results of validation using the R-index for very high and high classes is 55% and ROC shows that of 96.4%, for the P show method of 98%. This landslide mapping can be used for disaster mitigation and disaster preparedness planning purposes.

scholarly journals A Novel Rule-Based Approach in Mapping Landslide Susceptibility

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2274 ◽  
Author(s):  
Majid Roodposhti ◽  
Jagannath Aryal ◽  
Biswajeet Pradhan
Keyword(s):  
Landslide Susceptibility ◽  
Area Under The Curve ◽  
Local Scale ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Operating Characteristics ◽  
Rule Based ◽  
Conditioning Factors ◽  
Rule Sets ◽  
Rule Based Approach

Despite recent advances in developing landslide susceptibility mapping (LSM) techniques, resultant maps are often not transparent, and susceptibility rules are barely made explicit. This weakens the proper understanding of conditioning criteria involved in shaping landslide events at the local scale. Further, a high level of subjectivity in re-classifying susceptibility scores into various classes often downgrades the quality of those maps. Here, we apply a novel rule-based system as an alternative approach for LSM. Therein, the initially assembled rules relate landslide-conditioning factors within individual rule-sets. This is implemented without the complication of applying logical or relational operators. To achieve this, first, Shannon entropy was employed to assess the priority order of landslide-conditioning factors and the uncertainty of each rule within the corresponding rule-sets. Next, the rule-level uncertainties were mapped and used to asses the reliability of the susceptibility map at the local scale (i.e., at pixel-level). A set of If-Then rules were applied to convert susceptibility values to susceptibility classes, where less level of subjectivity is guaranteed. In a case study of Northwest Tasmania in Australia, the performance of the proposed method was assessed by receiver operating characteristics’ area under the curve (AUC). Our method demonstrated promising performance with AUC of 0.934. This was a result of a transparent rule-based approach, where priorities and state/value of landslide-conditioning factors for each pixel were identified. In addition, the uncertainty of susceptibility rules can be readily accessed, interpreted, and replicated. The achieved results demonstrate that the proposed rule-based method is beneficial to derive insights into LSM processes.

scholarly journals Landslide Susceptibility Mapping for Austria Using Geons and Optimization with the Dempster-Shafer Theory

2019 ◽  
Vol 9 (24) ◽  
pp. 5393 ◽  
Author(s):  
Thimmaiah Gudiyangada Nachappa ◽  
Sepideh Tavakkoli Piralilou ◽  
Omid Ghorbanzadeh ◽  
Hejar Shahabi ◽  
Thomas Blaschke
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Operating Characteristics ◽  
Scale Parameters ◽  
Dempster Shafer Theory ◽  
Object Based ◽  
Shafer Theory ◽  
The Impact

Landslide susceptibility mapping (LSM) can serve as a basis for analyzing and assessing the degree of landslide susceptibility in a region. This study uses the object-based geons aggregation model to map landslide susceptibility for all of Austria and evaluates whether an additional implementation of the Dempster–Shafer theory (DST) could improve the results. For the whole of Austria, we used nine conditioning factors: elevation, slope, aspect, land cover, rainfall, distance to drainage, distance to faults, distance to roads, and lithology, and assessed the performance and accuracy of the model using the area under the curve (AUC) for the receiver operating characteristics (ROC). We used three scale parameters for the geons model to evaluate the impact of the scale parameter on the performance of LSM. The results were similar for the three scale parameters. Applying the Dempster–Shafer theory could significantly improve the results of the object-based geons model. The accuracy of the DST-derived LSM for Austria improved and the respective AUC value increased from 0.84 to 0.93. The resulting LSMs from the geons model provide meaningful units independent of administrative boundaries, which can be beneficial to planners and policymakers.

scholarly journals Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1402 ◽  
Author(s):  
Nohani ◽  
Moharrami ◽  
Sharafi ◽  
Khosravi ◽  
Pradhan ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Aerial Photographs ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Bivariate Models

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

scholarly journals Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 762 ◽  
Author(s):  
Renwei Li ◽  
Nianqin Wang
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Statistical Models ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Aerial Photographs ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Topographic Wetness Index

The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.

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