Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran

2018 ◽  
Vol 93 (3) ◽  
pp. 1379-1418 ◽  
Author(s):  
Kourosh Shirani ◽  
Mehrdad Pasandi ◽  
Alireza Arabameri
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Landslide Susceptibility Assessment ◽  
Index Of Entropy ◽  
Southwestern Iran

scholarly journals Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia

2018 ◽  
Vol 10 (10) ◽  
pp. 1527 ◽  
Author(s):  
Dieu Tien Bui ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
Kamran Chapi ◽  
Mohsen Alizadeh ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Landslide Susceptibility ◽  
Support Vector ◽  
Susceptibility Assessment ◽  
Validation Process ◽  
Landslide Susceptibility Assessment ◽  
Tropical Environments ◽  
Index Of Entropy ◽  
Svm Model ◽  
Cameron Highlands

Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.

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.

Discriminant analysis as an efficient method for landslide susceptibility assessment in cities with the scarcity of predisposition data

2021 ◽  
Author(s):  
Cahio Guimarães Seabra Eiras ◽  
Juliana Ribeiro Gonçalves de Souza ◽  
Renata Delicio Andrade de Freitas ◽  
César Falcão Barella ◽  
Tiago Martins Pereira
Keyword(s):  
Discriminant Analysis ◽  
Efficient Method ◽  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Landslide Susceptibility Assessment
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