| Citation: |
ZHANG Wenlong, ZHANG Zhenkai, YANG Shuai. 2023. Study on Intelligent Evaluation and Zoning of Geohazards Risk in Mianluening Area. Northwestern Geology, 56(1): 276-283. doi: 10.12401/j.nwg.2022017
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Study on Intelligent Evaluation and Zoning of Geohazards Risk in Mianluening Area
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Abstract
Mianluening, located in Qinba mountain area, is one of the areas with the most serious geohazards in Shaanxi Province. This paper adopts 12 factors closely related to the occurrence of geohazards to construct a sample set based on GIS and machine learning technology, and then selects five machine learning algorithms to model the risk of geohazards in Mianluening area. The experiments demonstrated that the Random Forests model is the most suitable one to simulate the occurance of geohazards in the study area. Further division of geohazards risk is then performed by the aforementioned model, which is useful to guide the geohazards investigation and prevention.
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Keywords:
- geohazards /
- risk evaluation /
- Mianluening area /
- Machine learning
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References
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ZHANG Wenlong, ZHANG Zhenkai, YANG Shuai. 2023. Study on Intelligent Evaluation and Zoning of Geohazards Risk in Mianluening Area. Northwestern Geology, 56(1): 276-283. doi: 10.12401/j.nwg.2022017
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Figure 1.
Characterization charts of twelve types of evaluation factors
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Figure 2.
Cumulative curve of distance probability of neighboring ground disaster points
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Figure 3.
Positive and negative samples distribution chart
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Figure 4.
ROC curves for five machine learning models
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Figure 5.
Evaluation factor importance ranking chart
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Figure 6.
Map of geological disaster hazard evaluation results in Mianlvening area
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Figure 7.
Overlay map of occurred geological disaster sites and evaluation results