[1]王 力a,b,胡文卓a,等.三峡库区塌岸灾害易发性评价方法——以奉节—云阳段为例[J].山地学报,2023,(4):554-570.[doi:10.16089/j.cnki.1008-2786.000770 ]
　WANG Lia,b,HU Wenzhuoa,et al.An Updated Method for Susceptibility Evaluation on Bank Collapses along the Fengjie-Yunyang Section of Three Gorges Reservoir, China[J].Mountain Research,2023,(4):554-570.[doi:10.16089/j.cnki.1008-2786.000770 ]
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三峡库区塌岸灾害易发性评价方法——以奉节—云阳段为例
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《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2023年第4期
页码:
554-570
栏目:
山地灾害
出版日期:
2023-07-25

文章信息/Info

Title:
An Updated Method for Susceptibility Evaluation on Bank Collapses along the Fengjie-Yunyang Section of Three Gorges Reservoir, China
文章编号:
1008-2786-(2023)4-554-17
作者:
王 力a; b; 胡文卓a; b; 王世梅a; b*; 李 玉a; b; 范志宏a; b
(三峡大学 a. 三峡库区地质灾害教育部重点实验室; b. 土木与建筑学院,湖北 宜昌 443002)
Author(s):
WANG Lia; b; HU Wenzhuoa; b; WANG Shimeia; b*; LI Yua; b; FAN Zhihonga; b
(a. Key Laboratory of Geological Hazards in Three Gorges Reservoir Area of Ministry of Education; b. College of Civil Engineering and Architecture, China Three Gorges University, Yichang 443002, Hubei, China)
关键词:
三峡库区;  塌岸;  易发性评价;  机器学习模型
Keywords:
Three Gorges Reservoir;  bank collapse;  susceptibility evaluation;  machine learning model
分类号:
P694
DOI:
10.16089/j.cnki.1008-2786.000770
文献标志码:
A
摘要:
塌岸灾害广泛分布于中国三峡库区。塌岸易发性评价对库区灾害防治具有重要意义。当前塌岸易发性评价的研究程度低,评价模型的适用性差,评价指标的选取依据不充分,并没有考虑波浪对库岸稳定性的影响。本研究以塌岸灾害广泛发育的三峡库区奉节段至云阳段为研究对象,考虑波浪和库岸形态对研究区塌岸发育的影响,提出江岸宽度、库岸形态、风速这三个指标,并基于研究区塌岸灾害发育和分布特征共选取8个影响因子,构建三峡库区塌岸灾害易发性评价指标体系; 采用三种机器学习模型,实现三峡库区塌岸灾害易发性分区及检验、模型精度预测、评价结果的分区和实地验证。结果表明:(1)江岸宽度、岸坡形态和风速等影响因子权重靠前,对奉节段塌岸发育的贡献较大;(2)ANN模型、RF模型与SVM模型的较高-高易发区灾害点数量分别占总灾害点的53.1%、48.9%和39.3%。ANN模型与RF模型塌岸强度从低至高依次增大,分区结果较为合理,而SVM模型分区结果不太理想。(3)ANN模型、RF模型和SVM模型的AUC值分别为0.798、0.793、0.766,三种机器学习模型的预测精度较为可靠;(4)RF模型高易发性区域最符合实际地质条件,其易发性区划结果更为合理。本研究成果可以深化对水库塌岸灾害易发性评价方法的认识,并为三峡库区地质灾害防治工作提供理论指导和技术参考。
Abstract:
Bank collapses are widely distributed in the Three Gorges Reservoir area of China. A proper evaluation of bank collapse susceptibility is of great significance to geo-disaster prevention. Unfortunately, evaluation-related research on bank collapse susceptibility in the Three Gorges Reservoir area had been poor performed; there were quite unresolved issues regarding the applicability of susceptibility models, or a correct selection of evaluation index; and in most evaluation models, there was no inclusion of the influence of shock wave on the stability of reservoir bank. In this case study, it conducted evaluation on bank collapse susceptibility in the Fengjie-Yunyang section of Three Gorges Reservoir, where extensive geo-disasters severely dominated. In consideration of the influence of shock waves and bank morphology on the formations of bank collapses in the area, an evaluation index system of bank collapse susceptibility was constructed, which included three indexes, e.g. riverbank width, reservoir bank shape and wind speed, and eight influencing factors based on observed geo-disaster occurrences. Three machine learning models(ANN, RF and SVM model)were used to accomplish the risk zoning of bank collapse susceptibility and verification in the Reservoir area, as well as the prediction of model accuracy. This study found that(1)influencing factors such as riverbank width, reservoir bank shape and wind speed had higher weights than others, contributing more to the development of bank collapses in the Fengjie-Yunyang section.(2)The numbers of bank collapses estimated by ANN, RF and SVM model in moderate high-high risk zones accounted for 53.1, 48.9 and 39.3 of total geo-disasters, respectively. The obtained collapse intensity by ANN model and RF model increased from low to high, and risk zoning results were reasonable, whereas SVM model was less than ideal.(3)AUC values obtained from ANN, RF and SVM were 0.798, 0.793 and 0.766, respectively, indicating the three machine learning models all had reliable prediction accuracy.(4)The high risk areas zoned by RF model were most in line with actual geological conditions, thereby receiving a more reasonable results. This study can provide theoretical guidance and technical reference for prevention and prediction of geological disasters in the Three Gorges Reservoir area.

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备注/Memo

备注/Memo:
收稿日期(Received date): 2023- 03- 02; 改回日期(Accepted date):2023- 08-27
基金项目(Foundation item): 国家自然科学基金重点项目(U21A2031); 中国博士后科学基金(2021M701969)。[Key Project of National Natural Science Foundation of China(U21A2031); China Postdoctoral Science Foundation(2021M701969)]
作者简介(Biography): 王力(1988-),男,湖北孝感人,博士,讲师,主要研究方向:地质灾害机理、预防与评价。[WANG Li(1988-), male, born in Xiaogan, Hubei province, Ph.D., lecturer, research on geological hazard mechanism, prevention and evaluation] E-mail: wangli_ctgu@126.com
*通讯作者(Corresponding author): 王世梅(1965-),女,博士,教授,主要研究方向:岩土基本理论与地质灾害防灾减灾。[WANG Shimei(1965-),female, Ph.D., professor, research on basic theory of rock and soil and disaster prevention and reduction of geological disasters] E-mail: 284480957@qq.com
更新日期/Last Update: 2023-07-30