Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree

• Main Authors: Chen, Wei, Zhao, Xia, Shahabi, Himan, Shirzadi, Ataollah, Khosravi, Khabat, Chai, Huichan, Zhang, Shuai, Zhang, Lingyu, Ma, Jianquan, Chen, Yingtao, Wang, Xiaojing, Ahmad, Baharin, Li, Renwei
• Format: Article
• Published: Taylor and Francis Ltd. 2019
• Subjects: TH434-437 Quantity surveying
• Online Access: http://eprints.utm.my/id/eprint/88728/; http://dx.doi.org/10.1080/10106049.2019.1588393

In this study, we introduced novel hybrid of evidence believe function (EBF) with logistic regression (EBF-LR) and logistic model tree (EBF-LMT) for landslide susceptibility modelling. Fourteen conditioning factors were selected, including slope aspect, elevation, slope angle, profile curvature, plan curvature, topographic wetness index (TWI), stream sediment transport index (STI), stream power index (SPI), distance to rivers, distance to faults, distance to roads, lithology, normalized difference vegetation index (NDVI), and land use. The importance of factors was assessed using correlation attribute evaluation method. Finally, the performance of three models was evaluated using the area under the curve (AUC). The validation process indicated that the EBF-LMT model acquired the highest AUC for the training (84.7%) and validation (76.5%) datasets, followed by EBF-LR and EBF models. Our result also confirmed that combination of a decision tree-logistic regression-based algorithm with a bivariate statistical model lead to enhance the prediction power of individual landslide models.