应急管理视角下特大库区滑坡科学观测与早期预警

doi:10.16265/j.cnki.issn1003-3033.2025.03.2006

中国安全科学学报 ›› 2025, Vol. 35 ›› Issue (3): 221-231.doi: 10.16265/j.cnki.issn1003-3033.2025.03.2006

应急管理视角下特大库区滑坡科学观测与早期预警

叶霄¹^,²(), 朱鸿鹄²^,³^,^**(), 田坤⁴, 李厚芝⁵, 张巍², 程刚⁶

¹ 南京信息工程大学应急管理学院,江苏南京 210044
² 南京大学地球科学与工程学院,江苏南京 210023
³ 江苏省大地感知与控灾工程研究中心,江苏南京 210023
⁴ 长江三峡勘测研究院有限公司,湖北武汉 430019
⁵ 中国地质科学院探矿工艺研究所,四川成都 611734
⁶ 华北科技学院计算机学院,河北廊坊 065201

收稿日期:2024-10-15 修回日期:2024-12-18 出版日期:2025-03-28
通信作者:
** 朱鸿鹄( 1979—),男,江苏苏州人,博士,教授,博士生导师,主要从事地质与岩土工程安全评价、工程地质界面力学性质方面的研究。E-mail: zhh@nju.edu.cn。

作者简介:

叶霄 (1993—),男,甘肃平凉人,博士,副教授,主要从事地质灾害智能监测预警与应急管理、极端气候工程地质作用与防灾减灾等方面的研究。E-mail: yexiao@nuist.edu.cn。

田坤，高级工程师；

李厚芝，高级工程师；

张巍，副教授；

程刚，副教授

基金资助:
国家自然科学基金杰出青年基金资助(42225702); 国家重点研发计划项目(2018YFC1505104); 南京信息工程大学人才启动经费资助(2025r064)

Scientific observation and early warning of extremely large reservoir landslides from perspective of emergency management

YE Xiao¹^,²(), ZHU Honghu²^,³^,^**(), TIAN Kun⁴, LI Houzhi⁵, ZHANG Wei², CHENG Gang⁶

¹ School of Emergency Management, Nanjing University of Information Science & Technology, Nanjing Jiangsu 210044, China
² School of Earth Sciences and Engineering, Nanjing University, Nanjing Jiangsu 210023, China
³ Jiangsu Engineering Research Center of Earth Sensing and Disaster Control, Nanjing Jiangsu 210023, China
⁴ Three Gorges Geotechnical Consultants Co., Ltd., Wuhan Hubei 430019, China
⁵ Institute of Exploration Technology, Chinese Academy of Geological Science, Chengdu Sichuan 611734, China
⁶ School of Computer Science, North China Institute of Science and Technology, Langfang Hebei 065201, China

Received:2024-10-15 Revised:2024-12-18 Published:2025-03-28

摘要/Abstract

摘要：

为增强极端气候下库区滑坡灾害风险的应对能力,利用多源监测数据和机器学习算法,构建一种滑坡多维科学观测和水文气象早期预警的框架。通过分析2个特大型库区滑坡的多年观测数据,包括Sentinel-1、全球导航卫星系统(GNSS)地表位移和光纤(FO)应变,识别滑坡变形的时空格局和主控因素。结合提升决策树(BDT)算法,提出基于滑带土体实时应变(RTS)的水文气象早期预警方法,并系统性地探讨极端气候背景下库区滑坡监测预警与应急管理的策略框架。结果表明:不同变形诱发机制的滑坡表现出不同的时空变形特征,滑坡活动及其演化与局部抗滑处置措施密切相关;滑坡变形具有分区独立的特点,且与极端气象水文条件高度相关;基于RTS的早期预警模型能提供明确的多因子协同预警阈值,强调应急响应导向的滑坡监测预警理念。

关键词: 应急管理, 库区滑坡, 科学观测, 早期预警, 极端气候

Abstract:

To enhance the ability to cope with reservoir landslide hazard risks under extreme climate, a framework for multi-dimensional scientific observation and hydrometeorological early warning was constructed using multi-source monitoring data and machine learning algorithms. The spatiotemporal pattern and main controlling factors of landslide deformation were identified by analyzing the multi-annual observations of the two landslide cases, involving Sentinel-1, global navigation satellite system (GNSS) surface displacement and fiber optic (FO) strain. Leveraging the boosting decision tree (BDT) algorithm, a hydrometeorological early warning method based on slip zone real-time strain (RTS) was proposed, and the generalized framework of monitoring, early warning and emergency management strategies for reservoir landslides was systematically discussed. The results indicate that landslides with different deformation mechanisms show different spatiotemporal deformation characteristics, and landslide activities are closely related to localized anti-sliding treatment measures. Landslide kinematics are characterized by subzone-independent displacements and their drivers, which are highly correlated with hydrometeorological extremes. The RTS-based early warning model provides specific hydrometeorological thresholds, emphasizing the emergency response-oriented landslide monitoring and early warning concept.

Key words: emergency management, reservoir landslide, scientific observation, early warning, extreme climate

中图分类号:

X935

叶霄, 朱鸿鹄, 田坤, 李厚芝, 张巍, 程刚. 应急管理视角下特大库区滑坡科学观测与早期预警[J]. 中国安全科学学报, 2025, 35(3): 221-231.

YE Xiao, ZHU Honghu, TIAN Kun, LI Houzhi, ZHANG Wei, CHENG Gang. Scientific observation and early warning of extremely large reservoir landslides from perspective of emergency management[J]. China Safety Science Journal, 2025, 35(3): 221-231.

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参考文献 21

[1]	FROUDE M J, PETLEY D N. Global fatal landslide occurrence from 2004 to 2016[J]. Natural Hazards and Earth System Sciences, 2018, 18: 2161-2181.
[2]	TANG Huiming, WASOWSKI J, JUANG C H. Geohazards in the Three Gorges Reservoir area, China-Lessons learned from decades of research[J]. Engineering Geology, 2019, 261: DOI: 10.1016/j.enggeo.2019.105267.
[3]	刘启源, 刘金程. 自然灾害视角下区域韧性评价方法及影响因素[J]. 中国安全科学学报, 2023, 33(5): 174-181. doi: 10.16265/j.cnki.issn1003-3033.2023.05.1892
	LIU Qiyuan, LIU Jincheng. Regional resilience evaluation methods and influencing factors from perspective of natural disasters[J]. China Safety Science Journal, 2023, 33(5): 174-181. doi: 10.16265/j.cnki.issn1003-3033.2023.05.1892
[4]	YIN Yueping, HUANG Bolin, WANG Wenpei, et al. Reservoir-induced landslides and risk control in Three Gorges project on Yangtze River, China[J]. Journal of Rock Mechanics and Geotechnical Engineering, 2016, 8: 577-595.
[5]	AGHAKOUCHAK A, HUNING L S, CHIANG F, et al. How do natural hazards cascade to cause disasters?[J]. Nature, 2018, 561(7724): 458-460.
[6]	朱鸿鹄. 工程地质界面: 从多元表征到演化机理[J]. 地质科技通报, 2023, 42(1): 1-20.
	ZHU Honghu. Engineering geological interface: from multivariate characterization to evolution mechanism[J]. Bulletin of Geological Science and Technology, 2023, 42(1): 1-20.
[7]	YE Xiao, ZHU Honghu, WANG Jia, et al. Towards hydrometeorological thresholds of reservoir-induced landslide from subsurface strain observations[J]. Science China Technological Sciences, 2024, 67(6): 1907-1922.
[8]	GONG Wenping, ZHANG Shaoyan, JUANG H C, et al. Displacement prediction of landslides at slope-scale: review of physics-based and data-driven approaches[J]. Earth-Science Reviews, 2024, 258: DOI: 10.1016/j.earscirev.2024.104948.
[9]	LUO Shilin, HUANG Da. Deformation characteristics and reactivation mechanisms of the Outang ancient landslide in the Three Gorges Reservoir, China[J]. Bulletin of Engineering Geology and the Environment, 2020, 79: 3943-3958. doi: 10.1007/s10064-020-01838-3
[10]	许强, 曾裕平, 钱江澎, 等. 一种改进的切线角及对应的滑坡预警判据[J]. 地质通报, 2009, 28(4): 501-505.
	XU Qiang, ZENG Yuping, QIAN Jiangpeng, et al. Study on an improved tangential angle and the corresponding landslide prewarning criteria[J]. Geological Bulletin of China, 2009, 28(4): 501-505.
[11]	HIDAYAT R, SUTANTO S J, HIDAYAH A, et al. Development of a landslide early warning system in Indonesia[J]. Geosciences, 2019, 9: DOI: 10.3390/geosciences9100451.
[12]	SEGONI S, ROSI A, ROSSI G, et al. Analysing the relationship between rainfalls and landslides to define a mosaic of triggering thresholds for regional-scale warning systems[J]. Natural Hazards and Earth System Sciences, 2014, 14: 2637-2648.
[13]	PERUCCACCI S, BRUNETTI M T, GARIANO S L, et al. Rainfall thresholds for possible landslide occurrence in Italy[J]. Geomorphology, 2017, 290: 39-57.
[14]	程刚, 王振雪, 李刚强, 等. 滑坡一体化全维度监测预警技术进展与展望[J]. 中国安全科学学报, 2023, 33(1): 105-114. doi: 10.16265/j.cnki.issn1003-3033.2023.01.1144
	CHENG Gang, WANG Zhenxue, LI Gangqiang, et al., Progress and prospect of integrated full-dimensional monitoring and early warning technology for landslides[J]. China Safety Science Journal, 2023, 33(1): 105-114. doi: 10.16265/j.cnki.issn1003-3033.2023.01.1144
[15]	朱建军, 胡俊, 李志伟, 等. InSAR滑坡监测研究进展[J]. 测绘学报, 2022, 51(10): 2001-2019. doi: 10.11947/j.AGCS.2022.20220294
	ZHU Jianjun, HU Jun, LI Zhiwei, et al. Recent progress in landslide monitoring with InSAR[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2001-2019. doi: 10.11947/j.AGCS.2022.20220294
[16]	PEI Huafu, ZHANG Feng, ZHU Honghu, et al. Development of a distributed three-dimensional inclinometer based on OFDR technology and the Frenet-Serret equations[J]. Measurement, 2023, 223: DOI: 10.1016/j.measurement.2023.113769.
[17]	YE Xiao, ZHU Honghu, WANG Jia, et al. Subsurface multi-physical monitoring of a reservoir landslide with the fiber-optic nerve system[J]. Geophysical Research Letters, 2022, 49(11): DOI: 10.1029/2022GL098211.
[18]	SEGUÍ C, VEVEAKIS M. Continuous assessment of landslides by measuring their basal temperature[J]. Landslides, 2021, 18: 3953-3961.
[19]	MA Junwei, TANG Huiming, LIU Xiao, et al. Establishment of a deformation forecasting model for a step-like landslide based on decision tree C5.0 and two-step cluster algorithms: a case study in the Three Gorges reservoir area, China[J]. Landslides, 2017, 14: 1275-1281.
[20]	TANYU B F, ABBASPOUR A, ALIMOHAMMADLOU Y, et al. Landslide susceptibility analyses using random forest, C4.5, and C5.0 with balanced and unbalanced datasets[J]. Catena, 2021, 203: DOI: 10.1016/j.catena.2021.105355.
[21]	FATHANI T F, KARNAWATI D, WILOPO W. An integrated methodology to develop a standard for landslide early warning systems[J]. Natural Hazards and Earth System Sciences, 2016, 16: 2123-2135.

序号	预测规则	精度/%	含义
1	若f_i>-0.880 m/d & 146.450 m<L_i≤149.505 m & R_i>57.9 mm,则DP=II	90.68	低水位快速下降+暴雨
2	若L_i≤169.700 m & R_i>1.8 mm & I_i>24.4 mm/h,则DP=II	70.53	中低水位+强降雨
3	若L_i≤169.700 m & R_i>1.8 mm & I_i>24.4 mm/h,则DP=II	70.48	中低水位+强降雨
4	若L_i≤169.700 m & I_i>0.8 mm/h & R_i>57.9 mm,则DP=II	61.30	中低水位+暴雨
5	若L_i≤169.700 m & I_i>0.8 mm/h & I_i>24.4 mm/h,则DP=II	74.27	中低水位+强降雨
6	若f_i≤0.140 m/d & L_i≤150.195 m & R_i>57.9 mm,则DP=II	80.34	低水位+暴雨

序号	预测规则	精度/%	含义
1	若f_i>0.375 m/d & L_i>153.140 m,则DP=II	86.94	水位连续上升
2	若L_i≤166.305 m & f_i>0.375 m/d,或L_i>166.305 m & f_i>0.230 m/d,则DP=II	73.86	水位连续上升
3	若L_i>166.305 m & f_i≤-0.445 m/d,或f_i>-0.445 m/d & L_i>173.910 m,则DP=II	69.10	高水位缓慢下降
4	若L_i≤164.785 m & f_i>0.375 m/d,或166.305 m<L_i≤168.255 m,则DP=II	65.65	水位连续上升或中高水位
5	若L_i≤166.305 m & f_i>0.375 m/d,或L_i>166.305 m & R_i>5.0 mm,则DP=II	72.63	水位上升或高水位
6	若153.140 m<L_i≤166.305 m & f_i>0.375 m/d,则DP=II	83.31	水位连续上升
7	若L_i>166.305 m & f_i>1.850 m,则DP=II	86.66	高水位快速上升

应急管理视角下特大库区滑坡科学观测与早期预警

Scientific observation and early warning of extremely large reservoir landslides from perspective of emergency management

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