Ecological Indicators (Jun 2024)

To explore the optimal solution of different mapping units and classifiers and their application in the susceptibility evaluation of slope geological disasters

  • Shaohan Zhang,
  • Shucheng Tan,
  • Haishan Wang,
  • Yiqi Shi,
  • Duanyu Ding,
  • Yongqi Sun,
  • Hongxia Gao

Journal volume & issue
Vol. 163
p. 112073

Abstract

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Slope geological disaster is a type of natural disaster that is prevalent in the plateau mountainous area. Its occurrence frequently brings substantial harm to the local human population and living environment. The aim of this research is to investigate the suitability of various mapping units for different classifiers in the process of creating susceptibility zoning maps for slope geological disasters and to evaluate the effectiveness of each model. Concurrently, the susceptibility map produced by the high-accuracy model will assist relevant authorities in conducting disaster management tasks and also serve as a reference for modeling the susceptibility assessment of environmental geological disasters in other mountainous regions. Fuyuan County of China was considered as the research area, based on two different mapping units and four classifiers. Nine factors leading to disasters, such as elevation and topographic relief, were chosen as the evaluation indexes. With the help of the ArcGIS platform, the zoning map of geo-logical disaster susceptibility is drawn. Ultimately, the accuracy of the evaluation results was verified by the receiver operating characteristic curve and the confusion matrix. The findings indicate that all approaches are capable of producing susceptibility maps for geological disasters. However, the outcomes from the four classifiers that utilize slope units are more precise and logical than those that employ grid units. Lithology and the water system emerge as the most significant factors causing disasters in the study area, while the influence of fault zones is found to be minimal. The integration of the slope unit with the random forest classifier achieves the highest accuracy in predictions, maximizing the capabilities of both, and presents a promising application in the susceptibility mapping of slope geological disasters.

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