A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment |
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| Authors: | Mousa Abedini Bahareh Ghasemian Ataollah Shirzadi Himan Shahabi Kamran Chapi Binh Thai Pham |
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| Institution: | 1. Department of Geomorphology, Faculty of Humanities, University of Mohaghegh Ardabili, Ardabil, Iran;2. Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran;3. Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran;4. Department of Geotechnical Engineering, University of Transport Technology, Ha Noi, Vietnam |
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| Abstract: | AbstractA novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC?=?0.930). However, RS model (AUROC?=?0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC?=?0.972), MB (AUROC?=?0.970) and AB (AUROC?=?0.957) models, respectively. |
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| Keywords: | Landslide machine learning Bayes-based theory meta-classifiers Iran |
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