Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process

Abstract Landslides are one of the most extensive and destructive geological hazards in the globe. Tripura, a north-eastern hilly state of India experiences landslides almost each year during monsoon season causing casualty and huge economic losses. Hence it is required to assess the landslide susceptibility of the area that would support in short and long term planning and mitigation. Analytic hierarchy process (AHP) integrated with geospatial technology has been adopted for landslide susceptibility mapping in the state. Eight influencing factors such as slope, lithology, drainage density, rainfall, land use land cover, distance from rivers and roads, and soil type were selected to map the landslide susceptibility. Landslide susceptibility index (LSI) was found to vary from 6.205 during monsoon to 1.427 during post-monsoon season. The LSI values were classified into very high, high, moderate, low and very low susceptibility. Landslide susceptibility maps for three different seasons, namely, pre-monsoon, monsoon and post-monsoon were prepared. The study showed that most of the areas of the state come under very low to moderate landslide susceptibility zones. Around 73.2% area of the state is found to be under low landslide susceptible zones during the pre-monsoon season, around 62% area is prone to landslides with moderate susceptibility during monsoon season and 68.5% area comes under landslides with low susceptibility zones during the post-monsoon season. The output of this study may be referred by the engineers and planners for the assessment, control and mitigation of landslides and development of basic infrastructure in the state.

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

Open Geosciences ◽

10.1515/geo-2019-0056 ◽

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.

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Spatial landslide susceptibility mapping using integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches

10.21203/rs.3.rs-299575/v1 ◽

2021 ◽

Author(s):

Sina Paryani ◽

Aminreza Neshat ◽

Biswajeet Pradhan

Keyword(s):

Decision Making ◽

Land Use ◽

Landslide Susceptibility ◽

Fuzzy Inference ◽

Landslide Susceptibility Mapping ◽

Multi Criteria Decision Making ◽

Inference System ◽

Susceptibility Maps ◽

Neuro Fuzzy ◽

Landslide Susceptibility Maps

Abstract Landslide is a type of slope processes causing a plethora of economic damage and loss of lives worldwide every year. This study aimed to analyze spatial landslide susceptibility mapping in the Khalkhal-Tarom Basin by integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches, i.e. the stepwise weight assessment ratio analysis (SWARA) and the new best-worst method (BWM) techniques. For this purpose, the first step was to prepare a landslide inventory map, which were then divided randomly by the ratio of 30/70 for model training and validation. Thirteen conditioning factors were used as slope angle, slope aspect, altitude, topographic wetness index (TWI), plan curvature, profile curvature, distance to roads, distance to streams, distance to faults, lithology, land use, rainfall and normalized difference vegetation index (NDVI). After the database was created, the BWM and the SWARA methods were utilized to determine the relationships between the sub-criteria and landslides. Finally, landslide susceptibility maps were generated by implementing ANFIS-SWARA and ANFIS-BWM hybrid models, and the ROC curve was employed to appraise the predictive accuracy of each model. The results showed that the areas under curves (AUC) for the ANFIS-SWARA and ANFIS-BWM models were 73.6% and 75% respectively, and that the novel BWM yielded more realistic relationships between effective factors and the landslides. As a result, it was more efficient in training the ANFIS. Evidently, the generated landslide susceptibility maps (LSMs) can be very efficient in managing land use and preventing the damage caused by the landslide phenomenon.

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GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

Mathematical Problems in Engineering ◽

10.1155/2021/9914650 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Trung-Hieu Tran ◽

Nguyen Duc Dam ◽

Fazal E. Jalal ◽

Nadhir Al-Ansari ◽

Lanh Si Ho ◽

...

Keyword(s):

Soft Computing ◽

Landslide Susceptibility ◽

Google Earth ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Statistical Measures ◽

Susceptibility Maps ◽

Predicted Values ◽

Landslide Susceptibility Maps ◽

Computing Models

The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also.

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

10.21203/rs.3.rs-1046034/v1 ◽

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%.

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Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

Symmetry ◽

10.3390/sym12030325 ◽

2020 ◽

Vol 12 (3) ◽

pp. 325 ◽

Cited By ~ 20

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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The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Entropy ◽

10.3390/e21040372 ◽

2019 ◽

Vol 21 (4) ◽

pp. 372 ◽

Cited By ~ 12

Author(s):

Zhongjun Ma ◽

Shengwu Qin ◽

Chen Cao ◽

Jiangfeng Lv ◽

Guangjie Li ◽

...

Keyword(s):

Landslide Susceptibility ◽

Northeast China ◽

Susceptibility Mapping ◽

Changbai Mountain ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Topographic Wetness Index ◽

Mountain Area ◽

Susceptibility Maps ◽

Landslide Susceptibility Maps

Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.

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A Comparative Study of Frequency Ratio, Shannon’s Entropy and Analytic Hierarchy Process (AHP) Models for Landslide Susceptibility Assessment

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10090603 ◽

2021 ◽

Vol 10 (9) ◽

pp. 603

Author(s):

Sandeep Panchal ◽

Amit Kr. Shrivastava

Keyword(s):

Analytic Hierarchy Process ◽

Landslide Susceptibility ◽

Frequency Ratio ◽

Shannon’S Entropy ◽

Analytic Hierarchy ◽

Shannon's Entropy ◽

Susceptibility Maps ◽

Roc Area ◽

Hierarchy Process ◽

Landslide Susceptibility Maps

Landslide susceptibility maps are very important tools in the planning and management of landslide prone areas. Qualitative and quantitative methods each have their own advantages and dis-advantages in landslide susceptibility mapping. The aim of this study is to compare three models, i.e., frequency ratio (FR), Shannon’s entropy and analytic hierarchy process (AHP) by implementing them for the preparation of landslide susceptibility maps. Shimla, a district in Himachal Pradesh (H.P.), India was chosen for the study. A landslide inventory containing more than 1500 landslide events was prepared using previous literature, available historical data and a field survey. Out of the total number of landslide events, 30% data was used for training and 70% data was used for testing purpose. The frequency ratio, Shannon’s entropy and AHP models were implemented and three landslide susceptibility maps were prepared for the study area. The final landslide susceptibility maps were validated using a receiver operating characteristic (ROC) curve. The frequency ratio (FR) model yielded the highest accuracy, with 0.925 fitted ROC area, while the accuracy achieved by Shannon’s entropy model was 0.883. Analytic hierarchy process (AHP) yielded the lowest accuracy, with 0.732 fitted ROC area. The results of this study can be used by engineers and planners for better management and mitigation of landslides in the study area.

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Landslide Susceptibility Mapping Using Integrated Methods: A Case Study in the Chittagong Hilly Areas, Bangladesh

Geosciences ◽

10.3390/geosciences10120483 ◽

2020 ◽

Vol 10 (12) ◽

pp. 483

Author(s):

Yasin Wahid Rabby ◽

Yingkui Li

Keyword(s):

Logistic Regression ◽

Landslide Susceptibility ◽

Regional Scale ◽

Safe Zone ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Prediction Rate ◽

Susceptibility Maps ◽

Integrated Methods ◽

Landslide Susceptibility Maps

Landslide susceptibility mapping is of critical importance to identify landslide-prone areas to reduce future landslides, causalities, and infrastructural damages. This paper presents landslide susceptibility maps at a regional scale for the Chittagong Hilly Areas (CHA), Bangladesh. The frequency ratio (FR) was integrated with the analytical hierarchy process (AHP) (FR_AHP) and logistic regression (LR) (FR_LR). A landslide inventory of 730 landslide locations and 13 landslide predisposing factors including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), land use/land cover, rainfall, distance from drainage network, distance from fault lines, lithology, and normalized difference vegetation index (NDVI) were used. Landslide locations were randomly split into training (80%) and validation (20%) sites to support the susceptibility analysis. A safe zone was determined based on a slope threshold for logistic regression using the exploratory data analysis. The same number of non-landslide locations were randomly selected from the safe zone to train the model (FR_LR). Success and prediction rate curves and statistical indices, including overall accuracy, were used to assess model performance. The success rate curves show that FR_LR showed the highest area under the curve (AUC) (79.46%), followed by the FR_AHP (77.15%). Statistical indices also showed that the FR_LR model gave the best performance as the overall accuracy was 0.86 for training and 0.82 for validation datasets. The prediction rate curve shows similar results. The correlation analysis shows that the landslide susceptibility maps produced by FR and FR_AHP are highly correlated (0.95). In contrast, the correlation between the maps produced by FR and FR_LR was relatively lower (0.85). It indicates that the three models are highly convergent with each other. This study’s integrated methods would be helpful for regional-scale landslide susceptibility mapping, and the landslide susceptibility maps produced would be useful for regional planning and disaster management of the CHA, Bangladesh.

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Performance Evaluation of GIS-Based Artificial Intelligence Approaches for Landslide Susceptibility Modeling and Spatial Patterns Analysis

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9070443 ◽

2020 ◽

Vol 9 (7) ◽

pp. 443 ◽

Cited By ~ 7

Author(s):

Xinxiang Lei ◽

Wei Chen ◽

Binh Thai Pham

Keyword(s):

Landslide Susceptibility ◽

Local Governments ◽

Training Data ◽

Machine Learning Techniques ◽

Landslide Susceptibility Mapping ◽

Training Dataset ◽

Validation Dataset ◽

Affecting Factors ◽

Susceptibility Maps ◽

Landslide Susceptibility Maps

The main purpose of this study was to apply the novel bivariate weights-of-evidence-based SysFor (SF) for landslide susceptibility mapping, and two machine learning techniques, namely the naïve Bayes (NB) and Radial basis function networks (RBFNetwork), as benchmark models. Firstly, by using aerial photos and geological field surveys, the 263 landslide locations in the study area were obtained. Next, the identified landslides were randomly classified according to the ratio of 70/30 to construct training data and validation models, respectively. Secondly, based on the landslide inventory map, combined with the geological and geomorphological characteristics of the study area, 14 affecting factors of the landslide were determined. The predictive ability of the selected factors was evaluated using the LSVM model. Using the WoE model, the relationship between landslides and affecting factors was analyzed by positive and negative correlation methods. The above three hybrid models were then used to map landslide susceptibility. Thirdly, the ROC curve and various statistical data (SE, 95% CI and MAE) were used to verify and compare the predictive power of the model. Compared with the other two models, the Sysfor model had a larger area under the curve (AUC) of 0.876 (training dataset) and 0.783 (validation dataset). Finally, by quantitatively comparing the susceptibility values of each pixel, the differences in spatial morphology of landslide susceptibility maps were compared, and the model was found to have limitations and effectiveness. The landslide susceptibility maps obtained by the three models are reasonable, and the landslide susceptibility maps generated by the SysFor model have the highest comprehensive performance. The results obtained in this paper can help local governments in land use planning, disaster reduction and environmental protection.

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Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping

Remote Sensing ◽

10.3390/rs10081252 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1252 ◽

Cited By ~ 50

Author(s):

Prima Kadavi ◽

Chang-Wook Lee ◽

Saro Lee

Keyword(s):

Machine Learning ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Learning Models ◽

Landslide Occurrence ◽

Susceptibility Maps ◽

Multiclass Classifier ◽

Landslide Susceptibility Maps ◽

Machine Learning Models

The main purpose of this study was to produce landslide susceptibility maps using various ensemble-based machine learning models (i.e., the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models) for the Sacheon-myeon area of South Korea. A landslide inventory map including a total of 762 landslides was compiled based on reports and aerial photograph interpretations. The landslides were randomly separated into two datasets: 70% of landslides were selected for the model establishment and 30% were used for validation purposes. Additionally, 20 landslide condition factors divided into five categories (topographic factors, hydrological factors, soil map, geological map, and forest map) were considered in the landslide susceptibility mapping. The relationships among landslide occurrence and landslide conditioning factors were analyzed and the landslide susceptibility maps were calculated and drawn using the AdaBoost, LogitBoost, Multiclass Classifier, and Bagging models. Finally, the maps were validated using the area under the curve (AUC) method. The Multiclass Classifier method had higher prediction accuracy (85.9%) than the Bagging (AUC = 85.4%), LogitBoost (AUC = 84.8%), and AdaBoost (84.0%) methods.

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