Debris Flow Susceptibility Evaluation in Meizoseismal Region: A Case Study in Jiuzhaigou, China

IF 4.1 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Journal of Earth Science Pub Date : 2024-02-14 DOI:10.1007/s12583-022-1803-1
Yongwei Li, Linrong Xu, Yonghui Shang, Shuyang Chen
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Abstract

Jiuzhaigou is situated on a mountain-canyon region and is famous for frequent tectonic activities. An abundance of loose co-seismic landslides and collapses were produced on gullies after the Jiuzhaigou Earthquake on August 8, 2017, which was served as material source for debris flow in later years. Debris flow appears frequently which are seriously endangering the safety of people’s lives and properties. Even the earliest debris flow appeared in areas where no case ever reported before. The debris flow susceptibility evaluation (DFSE) is used for predicting the areas prone to debris flow, which is urgently required to avoid hazards and help to guide the strategy of preventive measures. Therefore, this work employs debris flow in Jiuzhaigou to reveal the characteristics of disaster-pregnant environment and to explore the application of machine learning in DFSE. Some new viewpoints are suggested: (i) Material density factor of debris flow is first adopted in this work, and it is proved to be a critical factor for triggering debris flows by sensitivity analysis method. (ii) Deep neural network and convolutional neural network (CNN) achieve relatively good area under the curve (AUC) values and are 0.021–0.024 higher than traditional machine learning methods. (iii) Watershed units combined with CNN-based model can achieve more accurate, reliable and practical susceptibility map. This work provides an idea for prevention of debris flow in mountainous lands.

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梅藏地震区泥石流易发性评估:中国九寨沟案例研究
九寨沟地处高山峡谷地带,是著名的构造活动频繁地区。2017 年 8 月 8 日九寨沟地震后,沟谷上产生了大量松散的同震滑坡和崩塌,成为后期泥石流的物质来源。泥石流频繁出现,严重危及人民生命财产安全。即使是最早出现泥石流的地区,以前也从未有过报道。泥石流易发性评估(DFSE)可用于预测泥石流易发地区,这对于避免危害和指导预防措施策略都是迫切需要的。因此,本研究以九寨沟泥石流为研究对象,揭示灾害易发环境的特征,并探索机器学习在 DFSE 中的应用。本文提出了一些新观点:(i) 首次采用泥石流的物质密度因子,并通过敏感性分析方法证明其是引发泥石流的关键因素。(ii) 深度神经网络和卷积神经网络(CNN)的曲线下面积(AUC)值相对较好,比传统机器学习方法高 0.021-0.024 倍。(iii) 流域单元与基于 CNN 的模型相结合,可以获得更加准确、可靠和实用的易感性地图。这项工作为山区泥石流的预防提供了一种思路。
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来源期刊
Journal of Earth Science
Journal of Earth Science 地学-地球科学综合
CiteScore
5.50
自引率
12.10%
发文量
128
审稿时长
4.5 months
期刊介绍: Journal of Earth Science (previously known as Journal of China University of Geosciences), issued bimonthly through China University of Geosciences, covers all branches of geology and related technology in the exploration and utilization of earth resources. Founded in 1990 as the Journal of China University of Geosciences, this publication is expanding its breadth of coverage to an international scope. Coverage includes such topics as geology, petrology, mineralogy, ore deposit geology, tectonics, paleontology, stratigraphy, sedimentology, geochemistry, geophysics and environmental sciences. Articles published in recent issues include Tectonics in the Northwestern West Philippine Basin; Creep Damage Characteristics of Soft Rock under Disturbance Loads; Simplicial Indicator Kriging; Tephra Discovered in High Resolution Peat Sediment and Its Indication to Climatic Event. The journal offers discussion of new theories, methods and discoveries; reports on recent achievements in the geosciences; and timely reviews of selected subjects.
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