Modeling of Debris Flow Susceptibility Assessment in Tianshan Based on Watershed Unit and Stacking Ensemble Algorithm

被引：2
作者：
Hou R. [1 ,2 ]
Li Z. [1 ,3 ]
Chen N. [1 ,4 ]
Tian S. [1 ,2 ]
Liu E. [5 ]
Ni H. [6 ]
机构：
[1] Key Laboratory of Mountain Hazards and Surface Processes, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu
[2] University of Chinese Academy of Sciences, Beijing
[3] College of Engineering, Tibet University, Lhasa
[4] Academy of Plateau Science and Sustainability, Xining
[5] State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu
[6] Institute of Exploration Technology, China Geological Survey, Chengdu
来源：
Diqiu Kexue - Zhongguo Dizhi Daxue Xuebao/Earth Science - Journal of China University of Geosciences | 2023年 / 48卷 / 05期
关键词：
debris flow; drought; hazard geology; machine learning; susceptibility; Tianshan; uplift;
D O I：
10.3799/dqkx.2022.271
中图分类号：
学科分类号：
摘要：
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. © 2023 China University of Geosciences. All rights reserved.
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页码：1892 / 1907
页数：15
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