优化神经网络下阿富汗东北高原寒旱区滑坡危险性评价

余波; 常鸣; 倪章; 孙文静; 徐恒志

doi:10.3799/dqkx.2022.392

Volume 48 Issue 5

May 2023

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Article Navigation > Earth Science > 2023 > 48(5): 1825-1835

Yu Bo, Chang Ming, Ni Zhang, Sun Wenjing, Xu Hengzhi, 2023. Landslide Hazard Assessment in Northeast Afghanistan Plateau Based on Optimized Neural Network. Earth Science, 48(5): 1825-1835. doi: 10.3799/dqkx.2022.392

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Yu Bo, Chang Ming, Ni Zhang, Sun Wenjing, Xu Hengzhi, 2023. Landslide Hazard Assessment in Northeast Afghanistan Plateau Based on Optimized Neural Network. Earth Science, 48(5): 1825-1835. doi: 10.3799/dqkx.2022.392

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Landslide Hazard Assessment in Northeast Afghanistan Plateau Based on Optimized Neural Network

doi: 10.3799/dqkx.2022.392

State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China

Received Date: 2022-07-13
Available Online: 2023-06-06
Publish Date: 2023-05-25

Abstract

Abstract

The northeastern part of Afghanistan is a typical cold and arid region where landslide geological hazards are developed. The landslide development is not only affected by topography, geological structure, human activities, and other factors, but also is controlled by snow cover, snow, and ice melt. In this paper, based on the primary data of remote sensing interpretation, considering the influence of snow cover and glacier activity on landslide development, two evaluation indexes of snow cover and ablation water equivalent were introduced to study the landslide risk in the cold and dry areas of the plateau. The landslide susceptibility evaluation system was established based on the weight of evidence and a fully connected neural network model. Degree-day model and SCS-CN model established the landslide risk evaluation system, and the evaluation model was tested according to the confusion matrix. The hazard assessment results show that the extremely high-risk area accounts for 10.46% of the total area, and the disaster area accounts for 82.71%, mainly distributed in the Kunar-Chitral reach in the east of Nuristan Province, the middle and eastern high mountains of Badakhshan Province except for Wakhan corridor section, and the Helmand Reach in Parwan Province. The high-risk area accounts for 14.83% of the total area, and the disaster area accounts for 12.11%, mainly distributed in the eastern region of Badakhshan Province, the western region of Nuristan Province, and Parwan Province. The test results and statistical results all show that the accuracy of the neural network is significantly improved by taking negative samples with a weight of evidence method. The research results can provide the scientific basis for Afghanistan's early warning and prevention of landslide geological disasters.
- Afghanistan,
- landslide,
- plateau cold and arid region,
- weight of evidence-fully connected neural network,
- hazard assessment,
- hazard geology

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Landslide Hazard Assessment in Northeast Afghanistan Plateau Based on Optimized Neural Network

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