Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier |
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Authors: | " target="_blank">Binh Thai Pham Indra Prakash Abolfazl Jaafari Dieu Tien Bui |
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Institution: | 1.Geotechnical Engineering and Artificial Intelligence research group (GEOAI),University of Transport Technology,Hanoi,Vietnam;2.Department of Science and Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG),Government of Gujarat,Gandhinagar,India;3.Young Researchers and Elite Club, Karaj Branch,Islamic Azad University,Karaj,Iran;4.Geographic Information Science Research Group,Ton Duc Thang University,Ho Chi Minh City,Vietnam;5.Faculty of Environment and Labour Safety,Ton Duc Thang University,Ho Chi Minh City,Vietnam |
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Abstract: | In this study, the spatial prediction of rainfall-induced landslides at the Pauri Gahwal area, Uttarakhand, India has been done using Aggregating One-Dependence Estimators (AODE) classifier which has not been applied earlier for landslide problems. Historical landslide locations have been collated with a set of influencing factors for landslide spatial analysis. The performance of the AODE model has been assessed using statistical analyzing methods and receiver operating characteristic curve technique. The predictive capability of the AODE model has also been compared with other popular landslide models namely Support Vector Machines (SVM), Radial Basis Function Neural Network (ANN-RBF), Logistic Regression (LR), and Naïve Bayes (NB). The result of analysis illustrates that the AODE model has highest predictability, followed by the SVM model, the ANN-RBF model, the LR model, and the NB model, respectively. Thus AODE is a promising method for the development of better landslide susceptibility map for proper landslide hazard management. |
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