ISSN 1003-8035 CN 11-2852/P
    李志,陈宁生,侯儒宁,等. 基于机器学习的伊犁河谷黄土区泥石流易发性评估[J]. 中国地质灾害与防治学报,2023,34(0): 1-12. DOI: 10.16031/j.cnki.issn.1003-8035.202301007
    引用本文: 李志,陈宁生,侯儒宁,等. 基于机器学习的伊犁河谷黄土区泥石流易发性评估[J]. 中国地质灾害与防治学报,2023,34(0): 1-12. DOI: 10.16031/j.cnki.issn.1003-8035.202301007
    LI Zhi,CHEN Ningshen,HOU Running,et al. Susceptibility Assessment of debris flow disaster based on machine learning models in the loess area of Yili valley[J]. The Chinese Journal of Geological Hazard and Control,2023,34(0): 1-12. DOI: 10.16031/j.cnki.issn.1003-8035.202301007
    Citation: LI Zhi,CHEN Ningshen,HOU Running,et al. Susceptibility Assessment of debris flow disaster based on machine learning models in the loess area of Yili valley[J]. The Chinese Journal of Geological Hazard and Control,2023,34(0): 1-12. DOI: 10.16031/j.cnki.issn.1003-8035.202301007

    基于机器学习的伊犁河谷黄土区泥石流易发性评估

    Susceptibility Assessment of debris flow disaster based on machine learning models in the loess area of Yili valley

    • 摘要: 伊犁河谷地处中-哈边境,南北疆结合带,是丝绸之路经济带的前沿,该区域生态环境脆弱,泥石流灾害多发。本研究采用随机森林(RF)、逻辑回归(LR)、支持向量机(SVM)以及决策树(DT)四种机器学习模型,模型输入为遥感判别和野外考察确定的398条泥石流沟以及14个特征参数,计算各个评价因子权重并对泥石流易发性进行评价,最后绘制ROC曲线以及计算曲线下面积(AUC)对四种机器学习的模型的准确性进行评价。研究结果表明:1.泥石流高易发区主要位于深切河谷地区的天山山地以及山前坡地的黄土覆盖区域;2.多年平均降雨量、干旱指数、地形起伏度是控制泥石流空间发育的前三个重要因素,3.四种模型的验证数据集AUC值分别为0.879(DT)、0.89 (LR)、0.938 (RF)、0.932 (SVM),随机森林模型在该区域的易发性评价中具有更好的预测能力;4.研究区黄土的生态植被被破坏是泥石流多发的重要原因,应该重点进行生态治理和保护,减少水土流失,从源头治理泥石流灾害。

       

      Abstract: The Yili Valley, located on the border between China and Kazakhstan, serves as the juncture of North and South Xinjiang, and stands as a pivotal outpost on the Silk Road Economic Belt. This area possesses a fragile ecological environment and experiences frequent debris flow disasters. In this study, four machine learning models--Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Decision Tree (DT)-- were employed to evaluate the debris flow susceptibility and compute the weights of evaluation factors. The models were fed inputs comprising 398 identified debris flow channels and 14 feature parameters such as fault density, topographic relief, land use, ndvi, multi-year average rainfall, etc obtained through remote sensing and field surveys. Also, the accuracy of the four machine learning models was evaluated by ROC curves and calculating the Area Under the Curve (AUC). The research results show that: 1. High debris flow susceptibility areas are mainly located in the Tianshan Mountains in the deep river valley region and the loess-covered areas in the mountain front slopes; 2. Multi-year average rainfall, drought index, and topographic relief variability are the top three influential factors controlling the spatial development of debris flows; 3. The AUC values for the validation datasets of the four models were 0.79 (DT), 0.89 (LR), 0.938 (RF) , and 0.932 (SVM), with the Random Forest model exhibiting superior predictive capability in assessing susceptibility in the region; 4. The disruption of ecological vegetation in the loess-covered region of the study area is a significant cause of frequent debris flow occurrences. Ecological governance and protection efforts should be emplasized to reduce soil erosion and effectively mitigate debris flow disasters at their source.

       

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