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

Panchal, Sandeep; Shrivastava, Amit Kumar

doi:10.3390/ijgi10090603

Cited by 40 publications

(20 citation statements)

References 42 publications

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“…Commonly, the landslide frequency increases as the distance from roads decreases. Therefore, the existing road and the on-going constructions disturb the stability of slope there by increasing the probability of landslide occurrence with results of other literature studies [19] [20]. According to [60], the landslides probability decreases with the increasing distance from river networks.…”

Section: Resultsmentioning

confidence: 94%

“…According to the importance of slope conditioning factor in the landslide occurrence, the study area was classi ed into ve classes in degree. With increase in slope angle, the possibility of landslide occurrence increases [47][48] [19]. Elevation is an important conditioning factor in landslide susceptibility mapping and it also impacts the environmental conditions on slopes such as human activity, vegetation, soil moisture and climate [49] [50].…”

Section: Landslide Conditioning Factorsmentioning

confidence: 99%

“…These models include the frequency ratio model [13][4] [14] [15] [16] [17] [2][18]. A combination of both frequency and Shannon entropy have been applied for landslide susceptibility mapping [19] [20][21] [22][23] [24],…”

Section: Introductionmentioning

confidence: 99%

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Landslide Susceptibility Mapping Using GIS and Bivariate Statistical Models in Chemoga Watershade, Ethiopia

Addis

2022

Preprint

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In this study, landslide susceptibility mapping was carried out for Chemoga watershade upper Abay basin in Ethiopia using a bivariate statistical models of Frequency Ratio (FR) and Information Values (IV). The main objective of this study is to identified landslide susceptibility areas using GIS and Bivariate Statistical Models. Based on Google Earth imagery and filed survey, about 169 landslides were identified and classified randomly into training landslides datasets (70%) and the remaining (30%) of landslides datasets were used for validation purpose. The 10 landslides conditioning factors like slope, elevation, aspect, curvature, Topographic Wetness Index, Normalized Difference Vegetation Index, road, river, land use and rainfall were integrated with training landslides to determine the weights of each landslide conditioning factor and factor classes using both frequency ratio and information value models. The landslide susceptibility maps were produced by overlay the weights of all the landslide conditioning factors using raster calculator of the spatial analyst tool in ArcGIS 10.4. The final landslide susceptibility maps were reclassified as very low, low, moderate, high and very high susceptibility classes both FR and IV models.This susceptibility maps were validated using landslide area under the curve (AUC). The results of AUC accuracy models showed that the success rates of the FR and IV models were 0.870 and 0.901, while the prediction rates were 0.880 and 0.923 respectively. The outcome of this paper will help decision-makers for land use planning and landslide mitigation purpose in this study area.

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Section: Resultsmentioning

confidence: 94%

Section: Landslide Conditioning Factorsmentioning

confidence: 99%

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Landslide Susceptibility Mapping Using GIS and Bivariate Statistical Models in Chemoga Watershade, Ethiopia

Addis

2022

Preprint

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“…This model creates a landslide prediction model in order to compute the susceptibility index. The information value model [ 11 , 12 ], the logistic regression model [ 13 , 14 , 15 ], the frequency ratio model [ 16 , 17 ], the weights of evidence method [ 18 , 19 , 20 ], the certainty factor method [ 21 , 22 ], etc., are widely utilized models. This method has the advantage of strong interpretability, allowing quantitative analysis of the close relationship between landslide and influencing factors and the mutual relationship between these variables.…”

Section: Introductionmentioning

confidence: 99%

Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China

Xiao

Zhao

et al. 2022

Sensors

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In complex mountainous areas where earthquakes are frequent, landslide hazards pose a significant threat to human life and property due to their high degree of concealment, complex development mechanism, and abrupt nature. In view of the problems of the existing landslide hazard susceptibility evaluation model, such as poor effectiveness and inaccuracy of landslide hazard data and the need for experts to participate in the calculation of a large number of evaluation factor weight classification statistics. In this paper, a combined SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and PSO-RF (Particle Swarm Optimization-Random Forest) algorithm was proposed to evaluate the susceptibility of landslide hazards in complex mountainous regions characterized by frequent earthquakes, deep river valleys, and large terrain height differences. First, the SBAS-InSAR technique was used to invert the surface deformation rates of the study area and identified potential landslide hazards. Second, the study area was divided into 412,585 grid cells, and the 16 selected environmental factors were analyzed comprehensively to identify the most effective evaluation factors. Last, 2722 landslide (1361 grid cells) and non-landslide (1361 grid cells) grid cells in the study area were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide and non-landslide data, the performances of the PSO-RF algorithm and three other machine learning algorithms, BP (back propagation), SVM (support vector machines), and RF (random forest) algorithms were compared. The results showed that 329 potential landslide hazards were updated using the surface deformation rates and existing landslide cataloguing data. Furthermore, the area under the curve (AUC) value and the accuracy (ACC) of the PSO-RF algorithm were 0.9567 and 0.8874, which were higher than those of the BP (0.8823 and 0.8274), SVM (0.8910 and 0.8311), and RF (0.9293 and 0.8531), respectively. In conclusion, the method put forth in this paper can be effectively updated landslide data sources and implemented a susceptibility prediction assessment of landslide disasters in intricate mountainous areas. The findings can serve as a strong reference for the prevention of landslide hazards and decision-making mitigation by government departments.

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“…Moreover, localized ground subsidence was also observed at historical karst collapse sites in Wuhan, similar to that of Elba Island, Italy [7] and Alagoas, Brazil [16]. For the vulnerability assessment of karst collapse in Wuhan, all the existing studies adopted the analytical hierarchy process (AHP) method [17,18], of which the hierarchy modeling only included the essential controlling conditions. Neither the external predisposing factors nor the contemporary ground subsidence map of Wuhan was considered in the modeling [11,19,20].…”

Section: Introductionmentioning

confidence: 99%

Karst Collapse Risk Zonation and Evaluation in Wuhan, China Based on Analytic Hierarchy Process, Logistic Regression, and InSAR Angular Distortion Approaches

Motagh

Wang

et al. 2021

Remote Sensing

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The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.911 compared to 0.812 derived from the AHP model. Both models performed well in identifying high-risk zones with only a 3% discrepancy in area. However, for the medium- and low-risk classes, although the spatial distribution of risk zoning results were similar between two approaches, the spatial extent of the risk areas varied between final models. The reliability of both methods were reduced significantly by excluding the InSAR-based ground subsidence map from the analysis, with the karst collapse presence falling into the high-risk zone being reduced by approximately 14%, and karst collapse absence falling into the karst area being increased by approximately 6.5% on the training samples. To evaluate the practicality of using only results from ground subsidence maps for the risk zonation, the results of AHP and LR are compared with a weighted angular distortion (WAD) method for karst risk zoning in Wuhan. We find that the areas with relatively large subsidence horizontal gradient values within the karst belts are generally spatially consistent with high-risk class areas identified by the AHP- and LR-based approaches. However, the WAD-based approach cannot be used alone as an ideal karst collapse risk assessment model as it does not include geological and natural factors into the risk zonation.

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

Cited by 40 publications

References 42 publications

Landslide Susceptibility Mapping Using GIS and Bivariate Statistical Models in Chemoga Watershade, Ethiopia

Landslide Susceptibility Mapping Using GIS and Bivariate Statistical Models in Chemoga Watershade, Ethiopia

Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China

Karst Collapse Risk Zonation and Evaluation in Wuhan, China Based on Analytic Hierarchy Process, Logistic Regression, and InSAR Angular Distortion Approaches

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