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

作者：Abedini, Mousa; Ghasemian, Bahareh; Shirzadi, Ataollah; Shahabi, Himan; Chapi, Kamran; Binh Thai Pham; Bin Ahmad, Baharin; Dieu Tien Bui^*

来源：Geocarto International, 2019, 34(13): 1427-1457.

DOI：10.1080/10106049.2018.1499820

• Landslide
• machine learning
• Bayes-based theory
• meta-classifiers
• Iran

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.

• 出版日期2019-11-10