Refined meteorological early warning for rainfall-induced landslide based on temporal probability model
SONG Yufei1, LI Xiang2, FAN Wen2, YU Ningyu2, CAO Yanbo2, DENG Longsheng2, TAO Hong3
(1. School of Data Science and Artificial Intelligence, Chang'an University, Xi'an, Shaanxi 710069, China; 2. Department of Geological Engineering, Chang'an University, Xi'an, Shaanxi 710069, China; 3. Shaanxi Institute of Geo-environment
Monitoring, Xi'an, Shaanxi 710054, China)
Abstract：The landslide meteorological early warning model based on empirical rainfall thresholds (ERT) often has a low warning accuracy, and the temporal probability model (TPM) is expected to address this shortcoming. To verify this hypothesis, a comparative experiment was conducted. First, we used accumulated effective rainfall-duration (EE-D) and same-day rainfall plus accumulated effective rainfall over the previous four days (R0-AE4) as variables to construct two sets of TPM models. The receiver operating characteristic (ROC) curve and correlation coefficient were then used to evaluate the discriminative and predictive abilities of ERT and TPM. Subsequently, the conditional probability formula was employed to couple the spatiotemporal probability of landslides, resulting in the proposal of a probabilistic landslide meteorological early warning model (P-LEWM). Finally, through simulated warnings, P-LEWM was compared with the matrix-based landslide early warning model (M-LEWM), which was constructed using ERT. The results indicate that: (1) The ERT/TPM constructed with R0-AE4 is more accurate in assessing the hazard level of rainfall-triggered landslides, with the area under the ROC curve increasing by 6.8% to 12.5% compared to EE-D. (2) The TPM proposed in this paper can accurately predict the probability of rainfall-triggered landslides, with a correlation coefficient between the predicted and recorded triggering-rainfall amounts exceeding 0.83. Additionally, the EE-D type TPM is more accurate for heavy rainfall prediction, while R0-AE4 is more suitable for regular rainfall events. (3) The EE-D type ERT tends to underestimate the hazard level of prolonged heavy rainfall in triggering landslides, causing M-LEWM to miss numerous landslides during two typical rainfall events in 2018, with a missed rate exceeding 50%, whereas P-LEWM constructed with TPM achieved a correct alert rate of over 90%. (4) Due to the accurate TPM and a reasonable spatiotemporal model coupling method, the correct alert rate of P-LEWM proposed in this paper has significantly improved compared to M-LEWM. The correct alert rate increased by 20.7% to 26.0%, the reasonable correct alert rate increased by 15.6% to 28.6%, and the missed alert rate decreased by more than 20.5%.
宋宇飞1，李 祥2，范 文2，于宁宇2，曹琰波2，邓龙胜2，陶 虹3. 基于时间概率模型的降雨型滑坡精细化气象预警研究[J]. 岩石力学与工程学报, 2025, 44(6): 1553-1568.
SONG Yufei1, LI Xiang2, FAN Wen2, YU Ningyu2, CAO Yanbo2, DENG Longsheng2, TAO Hong3. Refined meteorological early warning for rainfall-induced landslide based on temporal probability model. , 2025, 44(6): 1553-1568.
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2025, Vol. 44 
