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 Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 884 ◽  
Author(s):  
Tingyu Zhang ◽  
Ling Han ◽  
Wei Chen ◽  
Himan Shahabi
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Shaanxi Province ◽  
Slope Aspect ◽  
Support Vector ◽  
Operating Characteristics ◽  
Study Region ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

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

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.

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.

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

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 372 ◽  
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.

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

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.

scholarly journals Developing a Dynamic Web-GIS Based Landslide Early Warning System for the Chittagong Metropolitan Area, Bangladesh

2018 ◽  
Vol 7 (12) ◽  
pp. 485 ◽  
Author(s):  
Bayes Ahmed ◽  
Md. Rahman ◽  
Rahenul Islam ◽  
Peter Sammonds ◽  
Chao Zhou ◽  
...  
Keyword(s):  
Metropolitan Area ◽  
Early Warning ◽  
Landslide Susceptibility ◽  
Early Warning System ◽  
Warning System ◽  
Web Gis ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
Landslide Early Warning ◽  
Landslide Susceptibility Maps

This article aims to develop a Web-GIS based landslide early warning system (EWS) for the Chittagong Metropolitan Area (CMA), Bangladesh, where, in recent years, rainfall-induced landslides have caused great losses of lives and property. A method for combining static landslide susceptibility maps and rainfall thresholds is proposed by introducing a purposely-build hazard matrix. To begin with, eleven factor maps: soil permeability; surface geology; landcover; altitude; slope; aspect; distance to stream; fault line; hill cut; road cut; and drainage network along with a detailed landslide inventory map were produced. These maps were used, and four methods were applied: artificial neural network (ANN); multiple regressions; principal component analysis; and support vector machine to produce landslide susceptibility maps. After model validation, the ANN map was found best fitting and was classified into never warning, low, medium, and high susceptibility zones. Rainfall threshold analysis (1960–2017) revealed consecutive 5-day periods of rainfall of 71–282 mm could initiate landslides in CMA. Later, the threshold was classified into three rainfall rates: low rainfall (70–160 mm), medium rainfall (161–250 mm), and high rainfall (>250 mm). Each landslide was associated with a hazard class (no warning vs. warning state) based on the assumption that the higher the susceptibility, the lower the rainfall. Finally, the EWS was developed using various libraries and frameworks that is connected with a reliable online-based weather application programming interface. The system is publicly available, dynamic, and replicable to similar contexts and is able to disseminate alerts five days in advance via email notifications. The proposed EWS is novel and the first of its kind in Bangladesh, and can be applied to mitigate landslide disaster risks.

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

2021 ◽  
Author(s):  
Nirmalya Kumar Nath ◽  
Balram Panigrahi ◽  
Sanjay Kumar Raul ◽  
Jagadish Ch. Paul
Keyword(s):  
Landslide Susceptibility ◽  
Monsoon Season ◽  
The State ◽  
Landslide Susceptibility Mapping ◽  
Economic Losses ◽  
Post Monsoon ◽  
Susceptibility Maps ◽  
Post Monsoon Season ◽  
Hierarchy Process ◽  
Landslide Susceptibility Maps

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.

scholarly journals Performance Evaluation and Comparison of Bivariate Statistical-Based Artificial Intelligence Algorithms for Spatial Prediction of Landslides

2020 ◽  
Vol 9 (12) ◽  
pp. 696
Author(s):  
Wei Chen ◽  
Zenghui Sun ◽  
Xia Zhao ◽  
Xinxiang Lei ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Spatial Prediction ◽  
Slope Aspect ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Bivariate Model ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

The purpose of this study is to compare nine models, composed of certainty factors (CFs), weights of evidence (WoE), evidential belief function (EBF) and two machine learning models, namely random forest (RF) and support vector machine (SVM). In the first step, fifteen landslide conditioning factors were selected to prepare thematic maps, including slope aspect, slope angle, elevation, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), plan curvature, profile curvature, land use, normalized difference vegetation index (NDVI), soil, lithology, rainfall, distance to rivers and distance to roads. In the second step, 152 landslides were randomly divided into two groups at a ratio of 70/30 as the training and validation datasets. In the third step, the weights of the CF, WoE and EBF models for conditioning factor were calculated separately, and the weights were used to generate the landslide susceptibility maps. The weights of each bivariate model were substituted into the RF and SVM models, respectively, and six integrated models and landslide susceptibility maps were obtained. In the fourth step, the receiver operating characteristic (ROC) curve and related parameters were used for verification and comparison, and then the success rate curve and the prediction rate curves were used for re-analysis. The comprehensive results showed that the hybrid model is superior to the bivariate model, and all nine models have excellent performance. The WoE–RF model has the highest predictive ability (AUC_T: 0.9993, AUC_P: 0.8968). The landslide susceptibility maps produced in this study can be used to manage landslide hazard and risk in Linyou County and other similar areas.

scholarly journals Comparison of rotation forest based intelligence models for landslide susceptibility modeling

2021 ◽  
Author(s):  
Xia Zhao ◽  
Wei Chen ◽  
Tao Li ◽  
Faming Huang ◽  
Chaohong Peng ◽  
...  
Keyword(s):  
Decision Tree ◽  
Landslide Susceptibility ◽  
Spatial Prediction ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Rotation Forest ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract The precision of landslide susceptibility assessment has always been the focus of landslide spatial prediction research. It can be considered as the possibility of landslide disaster under the action of human activities or natural factors, or both of them. For the further exploration of the mechanism of this process, Muchuan County was proposed as the study area, and four well-known machine learning models, namely rotation forest (RF), J48 decision tree (J48), alternating decision tree (ADTree) and random forest (RaF), and their ensembles (RF-J48, RF-ADTree and RF-RaF) were introduced to explore the mechanism. These models are established by twelve landslide conditioning factors, which are selected based to the local special geological environment conditions and previous related researches, including plan curvature, profile curvature, slope angle, slope aspect, elevation, topographic wetness index (TWI), land use, normalized difference vegetation index (NDVI), soil, lithology, distance to roads, and distance to rivers, as well as training (195 landslides) and validation (84 landslides) datasets were developed. The landslide prediction performance of the above conditioning factors was analyzed through the correlation attribute evaluation (CAE) model. Then, through the landslide susceptibility maps made by the above six different models, the Jenks natural breaks method is used to divide the landslide susceptibility into five grades, which are very low, low, moderate, high, and very high. In addition, the accuracy of the above six landslide susceptibility maps was verified by implementing the relative operating characteristic curve (ROC) and the area under the ROC (AUC). That is, the capabilities of the above six models are compared and verified in the landslide spatial prediction. Finally, the obtained results show that elevation, lithology and TWI are the three most principal landslide conditioning factors in this research. The RF-RaF and RaF models in the training dataset performed best, with the AUC value of 0.75, while the RF-ADTree model (0.74), RF-J48 model (0.74), ADTree model (0.71) and J48 model (0.70) performed poorly. Meanwhile, similar results also emerge from the validation dataset, in which the RF-RaF model acquired the best performance (0.82) and the rest are the RF-ADTree model (0.80), RaF model (0.79), RF-J48 model (0.77), ADTree model (0.76) and J48 model (0.71). Last but by no means the least, the results can provide scientific references for local natural resources departments.

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