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https://doi.org/10.5194/nhess-2020-251
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-251
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Aug 2020

27 Aug 2020

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This preprint is currently under review for the journal NHESS.

Landslide susceptibility assessment based on different machine-learning methods in Zhaoping County of eastern Guangxi

Chunfang Kong1,2,3,4, Kai Xu1,2,3, Junzuo Wang1, Chonglong Wu1,2,3, and Gang Liu1,2 Chunfang Kong et al.
  • 1School of Computer, China University of Geosciences, Wuhan, 430074, China
  • 2Hubei Key Laboratory of Intelligent Geo-Information Processing, Wuhan, 430074, China
  • 3Innovation Center of Mineral Resources Exploration Engineering Technology in Bedrock Area, Ministry of Natural Resources, Guiyang, 550081, China
  • 4National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns, Hengyang, 421000, China

Abstract. Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study were implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility by Zhaoping County. To this end, 10 landslide disaster-related causal variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were selected for running four machine-learning (ML) methods, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, and field investigation were performed to verify the efficiency of these models. Analysis and comparison of the results denoted that all four ML models performed well for the landslide susceptibility evaluation as indicated by the values of ROC curves – from 0.863 to 0.934. Moreover, the results also indicated that the PSO algorithm has a good effect on SVM and FR models. In addition, such a result also revealed that the PSO-RF and PSO-SVM models have the strong robustness and stable performance, and those two models are promising methods that could be transferred to other regions for landslide susceptibility evaluation.

Chunfang Kong et al.

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Landslide Disaster Assessment Factor Data Set for Zhaoping county, Guangxi Province, China Kai Xu https://doi.org/10.4121/12857417.v1

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Short summary
This study compares four machine learning models in predicting and evaluating landslides susceptibility in Zhaoping County, Guangxi Province, China. The result shows that the PSO-RF and PSO-SVM models have strong robustness and stable performance, and those two models are promising methods that could be transferred to other regions for landslide susceptibility evaluation.
This study compares four machine learning models in predicting and evaluating landslides...
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