Preprints
https://doi.org/10.5194/nhess-2020-251
https://doi.org/10.5194/nhess-2020-251
27 Aug 2020
 | 27 Aug 2020
Status: this preprint was under review for the journal NHESS but the revision was not accepted.

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

Chunfang Kong, Kai Xu, Junzuo Wang, Chonglong Wu, and Gang Liu

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Chunfang Kong, Kai Xu, Junzuo Wang, Chonglong Wu, and Gang Liu
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Chunfang Kong, Kai Xu, Junzuo Wang, Chonglong Wu, and Gang Liu

Data sets

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

Chunfang Kong, Kai Xu, Junzuo Wang, Chonglong Wu, and Gang Liu

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Latest update: 26 May 2024
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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.
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