GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models

The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.

Greater Mumbai is one of the major sufferers of urban flood in India. In the present study, flood susceptibility zonation (FSZ) mapping of Greater Mumbai were performed using Geographical Information Systems (GIS) based models, evidential belief function (EBF), frequency ratio (FR), fuzzy gamma operator (FGO). For FSZ mapping, 12 conditioning factors were considered which included elevation, slope, plan curvature, topographic wetness index, stream power index, built-up index, geology, soil type, land use land cover, modified Fournier index, proximity to natural drainage and proximity to sewers and storm water drainage. The flood inventory database was divided into training and test data. The factor maps were integrated using GIS. The FSZ map was validated using area under curve (AUC) method. The AUC values of prediction rate using test data for FR, FGO, and EBF models were 0.7400, 0.7231 and 0.7268 respectively. The accuracy assessment of all three models shows fair prediction accuracy.

The exploitation of a suitable cartography of a well-identified geographical territory inevitably passes by reliefs drawn by the movements of ground. The most frequent of these movements are landslides such as the case of the Taounate city in the mountainous area of northern Morocco. The aim of the work is to develop a landslide susceptibility map of Taounate city based on the evidence belief function (EBF) and the frequency ratio (FR) Models. The landslide inventory map was prepared using interpretation of aerial photographs, satellite images and field visits. Twelve predisposition and release factors (slope, lithology, aspect, annual precipitation, elevation, curvature, normalized difference vegetation index (NDVI), land cover, distance from rivers, and distance from roads, topographic wetness index (TWI), and surface roughness) are taken into account in this study to develop susceptibility maps. The validation of the resulting susceptibility maps was obtained by analyzing the area under the curve. The results of the validation showed that the combination of the predictive factors: terrain slope, terrain aspect, lithology, TWI and NDVI proved to be the best combination for both FR and EBF models and give values for the area under the curve equal to 98% (0.9779) and 87% (0.8712) respectively. These coefficients reflect the good degree of reliability of the models used.

A Geo-statistical Dempster-Shafer Theory Evidential Belief Function (DST-EBF) model is selected for Landslide susceptibility evaluation. The aim of the study is to delineate zones prone to landslides within the Idukki district, Kerala using the EBF model and Geographical Information System (GIS) Technique. The objective is to integrate diverse datasets to assess and predict the landslide-prone areas, providing valuable insights for risk management and mitigation startgies. Topographical, Anthropogenic, and Geological factors are considered Landslide Conditioning Factors (LCFs), and Landslide Inventory data are used to establish and validate the Suscebtlity zones. Landslide inventory data is randomly divided into Training(70%) and Testing (30%) data. The resultant Landslide Susceptibility map is categorized into five zones using natural breaking classification method: Very low (20.96%), Low (29.71%), Medium (24.50%), High (18.63%), and Very high (6.20%), respectively, within the study area. The success rate and prediction rate were calculated using the AUC_ROC method and the EBF Model achieved the highest precision with a success rate of 0.935, and a prediction rate of 0.943 in the current study.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.

The rapid increase in human population has increased the groundwater resources demand for drinking, agricultural and industrial purposes. The main purpose of this study is to produce groundwater potential map (GPM) using weights-of-evidence (WOE) and evidential belief function (EBF) models based on geographic information system in the Azna Plain, Lorestan Province, Iran. A total number of 370 groundwater wells with discharge more than 10 m3s−1were considered and out of them, 256 (70%) were randomly selected for training purpose, while the remaining114 (30%) were used for validating the model. In next step, the effective factors on the groundwater potential such as altitude, slope aspect, slope angle, curvature, distance from rivers, drainage density, topographic wetness index, fault distance, fault density, lithology and land use were derived from the spatial geodatabases. Subsequently, the GPM was produced using WOE and EBF models. Finally, the validation of the GPMs was carried out using areas under the ROC curve (AUC). Results showed that the GPM prepared using WOE model has the success rate of 73.62%. Similarly, the AUC plot showed 76.21% prediction accuracy for the EBF model which means both the models performed fairly good predication accuracy. The GPMs are useful sources for planners and engineers in water resource management, land use planning and hazard mitigation purpose.

In the mountainous area of southwest China, there is a large number of large hydropower stations that have already been built or under construction. The construction and the safety of the operation of hydropower stations directly affect the residents’ safety of life and property on both sides of the downstream river. Landslide hazards are one of the main problems affecting the construction and operation of hydropower stations. Therefore, it is currently more urgent to evaluate landslide susceptibility within the basin. The goal of this paper is to carry out landslide susceptibility mapping that affects the construction and operation of hydropower stations. In the case of a large research area and lack of landslide hazard data, this paper selects the sample of blocking river landslide in a larger area with similar topography and geological conditions and applies the quantitative method to carry out landslide sensitivity mapping. In this paper, the slope unit works as the evaluation unit, 191 landslide sample data were obtained, and nine landslide conditioning factors (slope, aspect, curvature, lithology, distance to fault, distance to the river, land use, soil, and the topographic wetness index (TWI)) were obtained. Combined with the quantitative frequency model (FR model), the landslide susceptibility map was obtained. The research process and results show that (1) when the study area is large, the landslide data is scarce, and it is difficult to get the landslide inventory map; the landslide samples can be selected in a larger area with similar topography and geological conditions. It is feasible to use the analysis data of landslide samples to evaluate the landslide susceptibility; (2) for specific industry applications such as hydropower station operation safety, the landslide evaluation range can be reduced by the spatial correlation between landslide aspect and river flow direction, which is beneficial to the management department to reduce the scope of attention; and (3) the verification results show that the slope unit can be used for quantitative evaluation of landslide sensitivity based on the FR model and can obtain the highest evaluation accuracy. The main advantage of this method is its capability to carry out landslide susceptibility mapping with quantitative methods and to ensure that higher mapping accuracy is obtained in areas where landslide hazard data is lacking.

The current research presents landslide susceptibility mapping by frequency ratio (FR) and weights of evidence (WoE) models based on geographic information system (GIS) and an assessment of their performances for the Shangzhou District of Shangluo City, China. Firstly, landslide locations of the study area were detected using aerial photographs as well as by carrying outfield survey. Then, a total of 145 landslides were mapped out of which 101 (70 % landslide locations) were randomly selected for training the models, and the remaining 44 (30 % landslide locations) were used for validating the models. The following ten landslide conditioning factors, such as slope aspect, curvature, slope angle, elevation, distance to rivers, distance to faults, lithology, peak ground acceleration, distance to roads and precipitation, were considered in this study. Subsequently, landslide susceptibility maps were produced using FR and WoE models in ArcGIS 10.0 software. The validation of landslide susceptibility maps were carried out using areas under the curve. The validation results showed that the training accuracy were 0.7635 (76.35 %) and 0.7450 (74.50 %) for the FR and WoE models, with predictive accuracy 0.7395 (73.95 %) and 0.7102 (71.02 %), respectively, indicating that landslide susceptibility mapping using FR model is more accurate than WoE model for the study area.

Frequency ratio and GIS-based evaluation of landslide susceptibility applied to cultural heritage assessment

To prepare landslide susceptibility map of the Shivkhola watershed, one of the landslide prone part of Darjiling Himalaya, RS and GIS tools were being used to integrate 10 landslide triggering parameters like lithology, slope angle, slope aspect, slope curvature, drainage density, lineament, upslope contributing area (UCA), road contributing area (RCA) settlement density, and land use and land cover (LULC). Analytical Hierarchy Process (AHP) was applied to quantify all the factors by estimating factors weight on MATLAB Software with reasonable consistency ratio (CR). Frequency ratio model (FR) was used to derive class frequency ratio or class weight incorporating both pixels with and without landslides and to determine the relative importance of individual classes. All the required data layers were prepared in consultation with SOI Topo-sheet (78B/5), LIIS-III Satellite Image (2010) by using Erdas Imagine 8.5, PCI Geomatica, and ARC GIS Software. The weighted linear combination (WLC) method was followed to combine factors weight and class weight and to determine the landslide susceptibility coefficient value (LSCV or ‘M’) on GIS platform. Greater the value of ‘M’, higher is the susceptibility of landslide. The Shivkhola watershed was classified into five landslide susceptibility zones by averaging window lengths of 3, 5, 7, and 9 and taking into account the landslide threshold boundaries value of 7.05, 9.29, 11.5, and 13.8. The overall classification accuracy rate is 92.22 % and overall Kappa statistics is 0.894. The elements like weighted LULC map, RCA (road contributing area) map and settlement density map were developed and their weighted linear combination was performed to prepare landslide risk exposure map. Then by integrating landslide susceptibility map and landslide risk exposure map landslide hazard risk co-efficient values were derived and a classification was incorporated on ARC GIS Platform to prepare landslide hazard risk map of the Shivkhola watershed. To evaluate the validity of the landslide hazard risk map, probability/chance of landslide hazard risk event has been estimated by means of frequency ratio (FR) between landslide hazard risk area (%) and number of risk events (%) for each landslide hazard risk class. Finally, an accuracy assessment was made through a comparative study between true GPS derived data and a set of randomly selected pixels points from the classified image corresponding to the true data from 50 locations on ERDAS Imagine (8.5) which depicts that the classification accuracy of the landslide hazard risk map was 92.89 with overall Kappa statistics of 0.8929.

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.

GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models

The purpose of current study is to produce groundwater qanat potential map using frequency ratio (FR) and Shannon's entropy (SE) models in the Moghan watershed, Khorasan Razavi Province, Iran. The qanat is basically a horizontal, interconnected series of underground tunnels that accumulate and deliver groundwater from a mountainous source district, along a water- bearing formation (aquifer), and to a settlement. A qanat locations map was prepared for study area in 2013 based on a topographical map at a 1:50,000-scale and extensive field surveys. 53 qanat locations were detected in the field surveys. 70 % (38 locations) of the qanat locations were used for groundwater potential mapping and 30 % (15 locations) were used for validation. Fourteen effective factors were considered in this investigation such as slope degree, slope aspect, altitude, topographic wetness index (TWI), stream power index (SPI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Using the above conditioning factors, groundwater qanat potential map was generated implementing FR and SE models, and the results were plotted in ArcGIS. The predictive capability of frequency ratio and Shannon's entropy models were determined by the area under the relative operating characteristic curve. The area under the curve (AUC) for frequency ratio model was calculated as 0.8848. Also AUC for Shannon's entropy model was 0.9121, which depicts the excellence of this model in qanat occurrence potential estimation in the study area. So the Shannon's entropy model has higher AUC than the frequency ratio model. The produced groundwater qanat potential maps can assist planners and engineers in groundwater development plans and land use planning.

Application of GIS-based data-driven bivariate statistical models for landslide prediction: a case study of highly affected landslide prone areas of Teesta River basin