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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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International Journal of Sediment Research 2018年第2期33卷 157-170页

作者： Binh Thai Pham Ataollah Shirzadi Dieu Tien Bui Indra Prakash M.B.Dholakia Department of Civil Engineering Gujarat Technological University Geographic Information System Group Department of Business and IT University College of Southeast Norway 5. Department of Science ε Technology Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG) 6. Department of Civil Engineering LDCE Gujarat Technological University Department of Geotechnical Engineering University of Transport Technology Department of Rangeland and Watershed Management College of Natural Resources University of Kurdistan

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), Na(?)ve 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.

关键词： Landslide GIS Rotation Forest radial base function neural network India

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