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

doi:10.16265/j.cnki.issn1003-3033.2025.03.2006

Abstract

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

CLC Number:

X935

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.

Figures/Tables 9

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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