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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

查看全文 作　　者：Binh Thai [1,2]Pham;Ataollah [3]Shirzadi;Dieu Tien [4]Bui;Indra [5]Prakash;[6]M.B.Dholakia 高影响力作者 机构地区：[1]Department of Civil Engineering, Gujarat Technological University;[2]Department of Geotechnical Engineering, University of Transport Technology;[3]Department of Rangeland and Watershed Management, College of Natural Resources, University of Kurdistan;[4]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高影响力机构 出　　处：《International Journal of Sediment Research》索引2018年第33卷第2期,共14页高影响力期刊 摘　　要：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：机器学习算法 神经网络 混合模型 危险性 旋转 山崩 基础 光线
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