ISSN 1003-8035 CN 11-2852/P
    赵佳忆,田述军,李凯,等. 基于不同机器学习模型的岷江上游地震前后泥石流易发性评价[J]. 中国地质灾害与防治学报,2024,35(1): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202306035
    引用本文: 赵佳忆,田述军,李凯,等. 基于不同机器学习模型的岷江上游地震前后泥石流易发性评价[J]. 中国地质灾害与防治学报,2024,35(1): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202306035
    ZHAO Jiayi,TIAN Shujun,LI Kai,et al. Susceptibility assessment of debris flow in the upper reaches of the Minjiang River before and after the Wenchuan earthquake based on different machine learning models[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202306035
    Citation: ZHAO Jiayi,TIAN Shujun,LI Kai,et al. Susceptibility assessment of debris flow in the upper reaches of the Minjiang River before and after the Wenchuan earthquake based on different machine learning models[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 1-9. DOI: 10.16031/j.cnki.issn.1003-8035.202306035

    基于不同机器学习模型的岷江上游地震前后泥石流易发性评价

    Susceptibility assessment of debris flow in the upper reaches of the Minjiang River before and after the Wenchuan earthquake based on different machine learning models

    • 摘要: 科学准确地绘制泥石流易发性区划图以及确定主控因子及其贡献率,是区域泥石流预警预报和风险管理的重要基础。文章以岷江上游为研究区,以小流域为评价单元,分别采用5种机器学习模型构建了泥石流易发性评价模型,对汶川大地震前、后岷江上游泥石流易发性和评价因子贡献率进行了定量分析。结果表明:(1)集成机器学习模型的ACCAUC值均高于浅层机器学习模型,其中随机森林模型在地震前、后泥石流易发性评价中表现最优;(2)震前、震后泥石流发生率均随易发性等级的提高逐渐增大,且等级越高增量越大,各等级震后泥石流发生率均高于震前;(3)地震前、后侵蚀传递系数的贡献率均显著高于其它因子,与汶川大地震地震烈度空间分布特征叠加,加剧了震后干流和支流泥石流由下游向上游发育程度逐渐降低的空间分布规律。

       

      Abstract: The scientific and accurate mapping of debris flow susceptibility and the determination of main control factors and their contribution rates are important foundations for regional debris flow warning and risk management. The article takes the upper reaches of the Minjiang River as the research area and small watersheds as evaluation units. Five machine learning models were used to construct an evaluation model for the susceptibility of debris flows in the upper reaches of the Minjiang River, and quantitative analysis was conducted on the susceptibility of debris flows and the contribution rate of evaluation factors before and after the Wenchuan earthquake. The results show that: (1) The ACC and AUC values of the integrated machine learning model are higher than those of the shallow machine learning model, and the random forest model performs best in the assessment of debris flow vulnerability before and after the earthquake; (2) The occurrence rate of debris flow before and after earthquakes gradually increases with the increase of susceptibility level, and the increment increases with the increase of the level. The occurrence rate of debris flow after each level of earthquake is higher than before the earthquake; (3) The contribution rate of the erosion transfer coefficient before and after the earthquake is significantly higher than other factors, which is superimposed with the spatial distribution characteristics of the Wenchuan earthquake intensity, exacerbating the spatial distribution pattern of the gradually decreasing development degree of debris flows in the main and tributaries from downstream to upstream after the earthquake.

       

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