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

Abstract

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.

Keywords:

• Landslide
• prediction power
• land use planning
• China

Acknowledgements

The authors wish to thank Prof. Lufei Yang (Northwest Nonferrous Survey and Engineering Company) for useful information provided.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China (Grant No. 41807192, 41602359, 41602212), China Postdoctoral Science Foundation (Grant No. 2018T111084, 2017M613168), Project funded by Shaanxi Province Postdoctoral Science Foundation (Grant No. 2017BSHYDZZ07), the Open Fund of Shandong Provincial Key Laboratory of Depositional Mineralization & Sedimentary Minerals (Grant No. DMSM2017029), the Doctoral Scientific Research Foundation of Xi’an University of Science and Technology (Grant No. 2013QDJ038), and the Universiti Teknologi Malaysia (UTM) based on a Research University Grant (Q.J130000.2527.17H84).