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基于流域单元和堆叠集成模型的天山地区泥石流易发性评估建模
侯儒宁, 李志, 陈宁生, 田树峰, 刘恩龙, 倪化勇
PDF(11741 KB)
PDF(11741 KB)
基于流域单元和堆叠集成模型的天山地区泥石流易发性评估建模
Modeling of Debris Flow Susceptibility Assessment in Tianshan Based on Watershed Unit and Stacking Ensemble Algorithm
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天山地区未来将成为国家重要战略交通、油气资源管道、城镇居民点建设的部署区域，对该区域泥石流灾害易发性评估使重大潜在泥石流灾害点的监测点布置以及防治更具针对性.集成学习算法可避免灾害易发性评估中算法选择困难的问题且可显著提高建模精度，但其在泥石流易发性评估中的应用仍然缺乏，可靠性有待检验.本研究基于流域单元采用堆叠集成算法评估天山地区的泥石流灾害易发性，选择干旱度、陡度指数等14个特征变量进行天山地区的泥石流易发性评估建模，比较了堆叠集成算法与独立异质算法建模的预测性能，最后探讨了天山地区泥石流灾害的控制因素.结果表明：（1）天山地区泥石流灾害高、极高易发性区域占比分别为17.06%和19.75%，集中分布在北天山北坡和南天山南坡.（2）堆叠集成算法预测率曲线AUC值为0.87，显著高于独立机器学习算法（0.79~0.81），比独立机器学习算法有更好的预测性能.（3）除去常规地形和降雨对天山地区泥石流的发育有显著控制作用外，干旱和隆升也对天山地区泥石流的发育有重要影响.结果不仅有助于天山地区泥石流灾害风险管理，还对各类机器学习模型评估干旱山区泥石流易发性的建模特征有启示意义.
The Tianshan Mountain and its surrounding areas will become the deployment areas of national important strategic transportation, oil and gas resources pipelines, and urban settlement construction in the future. The risk prediction and assessment of debris flow disasters in the region will make the monitoring layout and prevention of major potential debris flow disaster points more targeted. The ensemble learning algorithm can avoid the difficulty of algorithm selection in disaster susceptibility assessment and significantly improve the modeling accuracy. However, its application in debris flow susceptibility assessment is still limited and its reliability needs to be tested. In this paper, the stacking ensemble algorithm was used to evaluate and predict the susceptibility of debris flow disasters in the Tianshan Mountain. Considering 14 characteristic variables such as drought degree and steepness index, the prediction performance of the stacking ensemble algorithm and the independent heterogeneous algorithm was compared. Finally, the control factors of debris flow disasters in the Tianshan area are discussed. The results show follows: (1) The areas with high debris flow disaster and extremely high susceptibility to debris flow in the Tianshan area account for 17.06% and 19.75%, respectively, and are concentrated on the northern slope of the North Tianshan and the southern slope of the South Tianshan. (2) The AUC value of the prediction rate curve of the stacked ensemble algorithm is 0.87, which is significantly higher than that of the independent machine learning algorithm (0.79-0.81) and has better prediction performance than the independent machine learning algorithm. (3) In addition to conventional topography and rainfall, which have significant control on the formation of debris flows in the Tianshan area, drought and uplift have important effects on the formation of debris flow in the Tianshan area. The results of this paper not only contribute to the risk management of debris flow disasters in the Tianshan area but also have implications for the assessment of debris flow susceptibility in arid mountainous areas.
天山 / 泥石流 / 机器学习 / 易发性 / 干旱 / 隆升 / 灾害地质
Tianshan / debris flow / machine learning / susceptibility / drought / uplift / hazard geology
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感谢两位匿名审稿专家和编委会对本文提出了宝贵的修改意见，同时感谢中国科学院天山冰川观测站及中国地质调查局提供的资料！
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