A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment. Issue 13 (10th November 2019)

• Record Type: Journal Article
• Authors: Abedini, Mousa; Ghasemian, Bahareh; Shirzadi, Ataollah; Shahabi, Himan; Chapi, Kamran; Pham, Binh Thai; Bin Ahmad, Baharin; Tien Bui, Dieu
• Is Part Of: Geocarto international. Volume 34:Issue 13(2019)
• Page Start: 1427
• Page End: 1457
• Publication Date: 2019-11-10
• Subjects: Landslide -- machine learning -- Bayes-based theory -- meta-classifiers -- Iran; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2018.1499820
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: 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.