A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment

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 gener...

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Bibliographic Details
Published in:Geocarto international 2019-11, Vol.34 (13), p.1427-1457
Main Authors: Abedini, Mousa, Ghasemian, Bahareh, Shirzadi, Ataollah, Shahabi, Himan, Chapi, Kamran, Pham, Binh Thai, Bin Ahmad, Baharin, Tien Bui, Dieu
Format: Article
Language:English
Subjects:
Bayes-based theory
Iran
Landslide
machine learning
meta-classifiers
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Summary: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.
ISSN:1010-6049
1752-0762
DOI:10.1080/10106049.2018.1499820