scholarly journals Frequency Ratio Density, Logistic Regression and Weights of Evidence Modelling for Landslide Susceptibility Assessment and Mapping in Yanase and Naka Catchments of Southeast Shikoku, Japan

2020 ◽  
Author(s):  
Matebie Meten ◽  
Netra Prakash Bhandary
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Weights Of Evidence ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Susceptibility Assessment ◽  
Landslide Susceptibility Assessment ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract Landslide susceptibility assessment is an important tool for disaster management and development activities. Shikoku Island in the southwest Japan is one of the most landslide prone areas due to heavy typhoon rainfall, complex geology and the presence of mountainous areas and low topographic features (valleys).Yanase and Naka Catchments of Shikoku Island in Japan were chosen as a study area. The objective of this study is to apply Frequency Ratio Densisty (FRD), Logistic Regression (LR) and Weights of Evidence (WoE) models in a GIS environment to prepare the landslide susceptibility maps of this area and select the best one for future infrastructure and landuse planning. Data layers including slope, aspect, profile curvature, plan curvature, lithology, land use, distance from river, distance from fault and annual rainfall were used in this study. In FR method, two models were attempted but the FRD model was found slightly better in its performance. In case of LR method, two models, one with equal proportion and the other with unequal proportion of landslide and non-landslide points were applied and the one with equal proportions was chosen based on its highest performance. A total of five landslide susceptibility maps(LSMs) were produced using FR, LR and WoE models resulting two, two and one LSMs respectively. However, one best model was chosen from the FR and LR methods based on the highest area under the curve (AUC) of the receiver operating characteristic (ROC) curves. This reduced the total number of landslide susceptibility maps to three with the success rates of 86.7%, 86.8% and 80.7% from FRD, LR and WoE models respectively. For validation purpose, all landslides were overlaid over the three landslide susceptibility maps and the percentage of landslides in each susceptibility class was calculated. The percentages of landslides that fall in the high and very high susceptibility classes of FRD, LR and WoE models showed 82%, 84% and 78% respectively. This showed that the LR model with equal proportions of landslides and non-landslide points was slightly better than FRD and WoE models in predicting the probability of future landslide occurrence.

scholarly journals Frequency Ratio Density, Logistic Regression and Weights of Evidence Modelling for Landslide Susceptibility Assessment and Mapping in Yanase and Naka Catchments of Southeast Shikoku, Japan

2020 ◽  
Author(s):  
Matebie Meten ◽  
Netra Prakash Bhandary
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Weights Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Equal Proportion ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract Landslide susceptibility mapping is an important tool for disaster management and development activities such as planning of transportation infrastructure, settlement and agriculture. Shikoku Island, which is found in the southwest of Japan, is one of the most landslide prone areas because of heavy typhoon rainfall, complex geology and the presence of mountainous areas and low topographic features (valleys).Yanase and Naka Catchments of Shikoku Island in Japan were chosen as a study area. Frequency Ratio Densisty (FRD), Logistic Regression (LR) and Weights of Evidence (WoE) models were applied in a GIS environment to prepare the landslide susceptibility maps of this area. Data layers including slope, aspect, profile curvature, plan curvature, lithology, land use, distance from river, distance from fault and annual rainfall were used in this study. In FR method, two models were attempted but the FRD model was found slightly better in its performance. In case of LR method, two models, one with equal proportion and the other with unequal proportion of landslide and non-landslide points were carried out and the one with equal proportions was chosen based on its highest performance. A total of five landslide susceptibility maps(LSMs) were produced using FR, LR and WoE models with two, two and one were attempted respectively. However, one best model was chosen from the FR and LR methods based on the highest area under the curve (AUC) of the receiver operating characteristic (ROC) curves. This reduced the total number of landslide susceptibility maps to three with the success rates of 86.7%, 86.8% and 80.7% from FRD, LR and WoE models respectively. For validation purpose, all landslides were overlaid over the three landslide susceptibility maps and the percentage of landslides in each susceptibility class was calculated. The percentages of landslides that fall in the high and very high susceptibility classes of FRD, LR and WoE models showed 82%, 84% and 78% respectively. This showed that the LR model with equal proportions of landslides and non-landslide points is slightly better than FRD and WoE models in predicting the future probability of landslide occurrence.

scholarly journals Landslide susceptibility assessment of the part of the North Anatolian Fault Zone (Turkey) by GIS-based frequency ratio and index of entropy models

2016 ◽  
Author(s):  
Gökhan Demir
Keyword(s):  
Fault Zone ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Susceptibility Assessment ◽  
North Anatolian Fault Zone ◽  
Landslide Susceptibility Assessment ◽  
The North ◽  
Index Of Entropy ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Abstract. Abstract: In the present study, landslide susceptibility assessment for the the part of the North Anatolian Fault Zone is made using index of entropy models within geographical information system. At first, the landslide inventory map was prepared in the study area using earlier reports, aerial photographs and multiple field surveys. 63 cases (69 %) out of 91 detected landslides were randomly selected for modeling, and the remaining 28 (31 %) cases were used for the model validation. The landslide-trigerring factors, including slope degree, aspect, elevation, distance to faults, distance to streams, distance to road. Subsequently, landslide susceptibility maps were produced using frequency ratio and index of entropy models. For verification, the receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) calculated. The verification results showed that frequency ratio model (AUC = 75.71 %) performed slightly better than index of entropy (AUC = 75.43 %) model. The interpretation of the susceptibility map indicated that distance to streams, distance to road and slope degree play major roles in landslide occurrence and distribution in the study area. The landslide susceptibility maps produced from this study could assist planners and engineers for reorganizing and planning of future road construction.

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.

Application of Fuzzy Weights of Evidence Method in Landslide Susceptibility Assessment Based on GIS

2013 ◽  
Vol 864-867 ◽  
pp. 2756-2759
Author(s):  
Zhi Wang Wang ◽  
Jian Hua Zhang ◽  
Duan You Li
Keyword(s):  
Landslide Susceptibility ◽  
Research Result ◽  
Slope Angle ◽  
Weights Of Evidence ◽  
Slope Aspect ◽  
Susceptibility Assessment ◽  
Gis Technology ◽  
Landslide Susceptibility Assessment ◽  
Rs And Gis ◽  
Fuzzy Weights

This paper deals with landslide hazards susceptibility assessment in the study area from Zigui to Badong counties in TGP reservoir region using RS and GIS technology. The causative factors including lithology, distance to faults, elevation, slope aspect, slope angle, drainage network, distance to river and distribution of plant are derived from geological map, Digital Elevation Model (DEM) and Spot imagery data using RS and GIS technology. The paper analyzes landslide susceptibility assessment using fuzzy weights of evidence method, which could combine knowledge-based fuzzy membership values with data-based conditional probabilities to improve the accuracy of landslide susceptibility assessment. The research result is very coincident with the occurrence of the known landslides in the study area.

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