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Title
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A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment
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Type
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JournalPaper
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Keywords
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Landslide, machine learning, Bayes-based theory, meta-classifiers, Iran
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Abstract
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A 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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Researchers
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DieuTien Bui (Not In First Six Researchers), Baharin Ben Ahmad (Not In First Six Researchers), Binh Thai Pham (Not In First Six Researchers), Kamran Chapi (Fifth Researcher), Himan Shahabi (Fourth Researcher), Ataollah Shirzadi (Third Researcher), Bahare Qasemyan (Second Researcher), Mosa Abdini (First Researcher)
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