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Water 2018, 10(8), 1019; https://doi.org/10.3390/w10081019

GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China

1
Department of Ecology, Jinan University, Guangzhou 510632, China
2
Department of Environmental Engineering, School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China
3
South China Institute of Environment Sciences, Ministry of Environment Protection of PRC, Guangzhou 510535, China
4
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
5
Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou 510632, China
*
Author to whom correspondence should be addressed.
Received: 10 May 2018 / Revised: 25 July 2018 / Accepted: 28 July 2018 / Published: 1 August 2018
(This article belongs to the Special Issue Water Quality: A Component of the Water-Energy-Food Nexus)
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Abstract

Landslide susceptibility assessment is presently considered an effective tool for landslide warning and forecasting. Under the assessment procedure, a credible index weight can greatly increase the rationality of the assessment result. Using the Beijiang River Basin, China, as a case study, this paper proposes a new weight-determining method based on random forest (RF) and used the weighted linear combination (WLC) to evaluate the landslide susceptibility. The RF weight and eight indices were used to construct the assessment model. As a comparison, the entropy weight (EW) and weight determined by analytic hierarchy process (AHP) were also used, respectively, to demonstrate the rationality of the proposed weight-determining method. The results show that: (1) the average error rates of training and testing based on RF are 18.12% and 15.83%, respectively, suggesting that the RF model can be considered rational and credible; (2) RF ranks the indices elevation (EL), slope (SL), maximum one-day precipitation (M1DP) and distance to fault (DF) as the Top 4 most important of the eight indices, occupying 73.24% of the total, while the indices runoff coefficient (RC), normalized difference vegetation index (NDVI), shear resistance capacity (SRC) and available water capacity (AWC) are less consequential, with an index importance degree of only 26.76% of the total; and (3) the verification of landslide susceptibility indicates that the accuracy rate based on the RF weight reaches 75.41% but are only 59.02% and 72.13% for the other two weights (EW and AHP), respectively. This paper shows the potential to provide a new weight-determining method for landslide susceptibility assessment. Evaluation results are expected to provide a reference for landslide management, prevention and reduction in the studied basin. View Full-Text
Keywords: landslide susceptibility; index weight; random forest; weighted linear combination; geographical information system; the Beijiang River Basin landslide susceptibility; index weight; random forest; weighted linear combination; geographical information system; the Beijiang River Basin
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Wang, P.; Bai, X.; Wu, X.; Yu, H.; Hao, Y.; Hu, B.X. GIS-Based Random Forest Weight for Rainfall-Induced Landslide Susceptibility Assessment at a Humid Region in Southern China. Water 2018, 10, 1019.

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