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 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 Improving Voting Feature Intervals for Spatial Prediction of Landslides

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Binh Thai Pham ◽  
Tran Van Phong ◽  
Mohammadtaghi Avand ◽  
Nadhir Al-Ansari ◽  
Sushant K. Singh ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Model Performance ◽  
Spatial Prediction ◽  
Susceptibility Assessment ◽  
Improve Performance ◽  
Landslide Susceptibility Assessment ◽  
Ensemble Techniques ◽  
Susceptibility Maps ◽  
Quantitative Validation ◽  
Two Hybrid

In this study, the main aim is to improve performance of the voting feature intervals (VFIs), which is one of the most effective machine learning models, using two robust ensemble techniques, namely, AdaBoost and MultiBoost for landslide susceptibility assessment and prediction. For this, two hybrid models, namely, AdaBoost-based Voting Feature Intervals (ABVFIs) and MultiBoost-based Voting Feature Intervals (MBVFIs) were developed and validated using landslide data collected from one of the landslide affected districts of Vietnam, namely, Muong Lay. Quantitative validation methods including area under the ROC curve (AUC) were used to evaluate model performance. The results indicated that both the newly developed ensemble models ABVFI (AUC = 0.859) and MBVFI (AUC = 0.839) outperformed the single VFI (AUC = 0.824) model. Thus, ensemble framework-based VFI algorithms can be used for the accurate spatial prediction of landslides, which can also be applied in other landslide prone regions of the world. Landslide susceptibility maps developed by ensemble VFI models can be used for better landslide prevention and risk management of the area.

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