A hybrid machine learning ensemble approach based on a Radial Basis Function neural network and Rotation Forest for landslide susceptibility modeling: A case study in the Himalayan area,India |
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Authors: | Binh Thai Pham Ataollah Shirzadi Dieu Tien Bui Indra Prakash M.B. Dholakia |
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Affiliation: | 1. Department of Civil Engineering, Gujarat Technological University, Nr. Visat Three Roads, Visat - Gandhinagar Highway, Chandkheda, Ahmedabad 382424, Gujarat, India;2. Department of Geotechnical Engineering, University of Transport Technology, 54 TrieuKhuc, ThanhXuan, Ha Noi, Viet Nam;3. Department of Rangeland and Watershed Management, College of Natural Resources, University of Kurdistan, Sanandaj, Iran;4. Geographic Information System Group, Department of Business and IT, University College of Southeast Norway, Gullbringvegen 36, N-3800 Bø i Telemark, Norway;5. Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, India;6. Department of Civil Engineering, LDCE, Gujarat Technological University, Ahmedabad - 380015, Gujarat, India |
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Abstract: | In this paper, a hybrid machine learning ensemble approach namely the Rotation Forest based Radial Basis Function (RFRBF) neural network is proposed for spatial prediction of landslides in part of the Himalayan area (India). The proposed approach is an integration of the Radial Basis Function (RBF) neural network classifier and Rotation Forest ensemble, which are state-of-the art machine learning algorithms for classification problems. For this purpose, a spatial database of the study area was established that consists of 930 landslide locations and fifteen influencing parameters (slope angle, road density, curvature, land use, distance to road, plan curvature, lineament density, distance to lineaments, rainfall, distance to river, profile curvature, elevation, slope aspect, river density, and soil type). Using the database, training and validation datasets were generated for constructing and validating the model. Performance of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), statistical analysis methods, and the Chi square test. In addition, Logistic Regression (LR), Multi-layer Perceptron Neural Networks (MLP Neural Nets), Naïve Bayes (NB), and the hybrid model of Rotation Forest and Decision Trees (RFDT) were selected for comparison. The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models (LR, MLP Neural Nets, NB, and RFDT); therefore, the proposed RFRBF model is promising and should be used as an alternative technique for landslide susceptibility modeling. |
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Keywords: | Landslide GIS Rotation Forest Radial Base Function Neural Network India |
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