Evaluation of Geological Disaster Sensitivity in Shuicheng District Based on the WOE-RF Model

Zefang Zhang, Zhikuan Qian (), Yong Wei, Xing Zhu and Linjun Wang
Additional contact information
Zefang Zhang: School of Civil Engineering and Architecture, Guizhou Minzu University, Guiyang 550025, China
Zhikuan Qian: School of Civil Engineering and Architecture, Guizhou Minzu University, Guiyang 550025, China
Yong Wei: School of Civil Engineering and Architecture, Guizhou Minzu University, Guiyang 550025, China
Xing Zhu: State Key Laboratory of Geological Disaster Prevention and Geological Environment Protection, Chengdu University of Technology, Chengdu 610059, China
Linjun Wang: School of Civil Engineering and Architecture, Guizhou Minzu University, Guiyang 550025, China

Sustainability, 2022, vol. 14, issue 23, 1-11

Abstract: To improve the prevention and control of geological disasters in Shuicheng District, 10 environmental factors—slope, slope direction, curvature, NDVI, stratum lithology, distance from fault, distance from river system, annual average rainfall, distance from road and land use—were selected as evaluation indicators by integrating factors such as landform, basic geology, hydrometeorology and engineering activities. Based on the weight of evidence, random forest, support vector machine and BP neural network algorithms were introduced to build WOE-RF, WOE-SVM and WOE-BPNN models. The sensitivity of Shuicheng District to geological disasters was evaluated using the GIS platform, and the region was divided into areas of extremely high, high, medium, low and extremely low sensitivity to geological disasters. By comparing and analyzing the ROC curve and the distribution law of the sensitivity index, the AUC evaluation accuracy of the WOE-RF, WOE-SVM and WOE-BPNN models was 0.836, 0.807 and 0.753, respectively; the WOE-RF model was shown to be the most effective. In the WOE-RF model, the extremely high-, high-, medium-, low- and extremely low-sensitivity areas accounted for 15.9%, 16.9%, 19.3%, 21.0% and 26.9% of the study area, respectively. The extremely high- and high-sensitivity areas are mainly concentrated in areas with large slopes, broken rock masses, river systems and intensive human engineering activity. These research results are consistent with the actual situation and can provide a reference for the prevention and control of geological disasters in this and similar mountainous areas.

Keywords: geological disasters; environmental factors; weight of evidence; random forest; sensitivity evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/23/16247/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/23/16247/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:23:p:16247-:d:994415

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().