ISSN 1009-6248CN 61-1149/P 双月刊

主管单位:中国地质调查局

主办单位:中国地质调查局西安地质调查中心
中国地质学会

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    张文龙, 张振凯, 杨帅. 勉略宁地区地质灾害危险性智能评价和区划研究[J]. 西北地质,2023,56(1): 276-283.
    引用本文: 张文龙, 张振凯, 杨帅. 勉略宁地区地质灾害危险性智能评价和区划研究[J]. 西北地质,2023,56(1): 276-283.
    ZHANG Wenlong, ZHANG Zhenkai, YANG Shuai. Study on Intelligent Evaluation and Zoning of Geohazards Risk in Mianluening Area[J]. Northwestern Geology,2023,56(1): 276-283.
    Citation: ZHANG Wenlong, ZHANG Zhenkai, YANG Shuai. Study on Intelligent Evaluation and Zoning of Geohazards Risk in Mianluening Area[J]. Northwestern Geology,2023,56(1): 276-283.

    勉略宁地区地质灾害危险性智能评价和区划研究

    Study on Intelligent Evaluation and Zoning of Geohazards Risk in Mianluening Area

    • 摘要: 勉略宁地处秦巴山区,是陕西省地质灾害发育最严重的地区之一。笔者基于GIS与机器学习技术,采用了与地质灾害发生密切相关的12种因子,通过构建样本集,选用5种机器学习算法进行勉略宁地区的地质灾害危险性建模。实验结果标明,随机森林模型能够更好地模拟勉略宁地区的地质灾害发生情况。通过该模型对勉略宁地区进行地质灾害危险区进一步划分,从而指导地质灾害调查与防治工作。

       

      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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