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Article

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

by 1,2,3 and 1,2,3,*
1
College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
2
Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an 710021, China
3
Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation, Xi’an 710054, China
*
Author to whom correspondence should be addressed.
Water 2020, 12(1), 113; https://doi.org/10.3390/w12010113
Received: 7 November 2019 / Revised: 26 December 2019 / Accepted: 26 December 2019 / Published: 29 December 2019
(This article belongs to the Section Hydrology)
In this study, Random SubSpace-based classification and regression tree (RSCART) was introduced for landslide susceptibility modeling, and CART model and logistic regression (LR) model were used as benchmark models. 263 landslide locations in the study area were randomly divided into two parts (70/30) for training and validation of models. 14 landslide influencing factors were selected, such as slope angle, elevation, aspect, sediment transport index (STI), topographical wetness index (TWI), stream power index (SPI), profile curvature, plan curvature, distance to rivers, distance to road, soil, normalized difference vegetation index (NDVI), land use, and lithology. Finally, the hybrid RSCART model and two benchmark models were applied for landslide susceptibility modeling and the receiver operating characteristic curve method is used to evaluate the performance of the model. The susceptibility is quantitatively compared based on each pixel to reveal the system spatial pattern between susceptibility maps. At the same time, area under ROC curve (AUC) and landslide density analysis were used to estimate the prediction ability of landslide susceptibility map. The results showed that the RSCART model is the optimal model with the highest AUC values of 0.852 and 0.827, followed by LR and CART models. The results also illustrate that the hybrid model generally improves the prediction ability of a single landslide susceptibility model. View Full-Text
Keywords: landslide susceptibility; hybrid models; random subspace; classification and regression tree landslide susceptibility; hybrid models; random subspace; classification and regression tree
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MDPI and ACS Style

Li, Y.; Chen, W. Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques. Water 2020, 12, 113. https://doi.org/10.3390/w12010113

AMA Style

Li Y, Chen W. Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques. Water. 2020; 12(1):113. https://doi.org/10.3390/w12010113

Chicago/Turabian Style

Li, Yang, and Wei Chen. 2020. "Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques" Water 12, no. 1: 113. https://doi.org/10.3390/w12010113

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