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March 10, 2026Scientific Reports0 citationsOpen Access

Analysis of rainfall response and graded warning for landslides

YXYin XingPWP. WangSHSaipeng Huang

Key Points

  • This research aims to develop an effective early warning model for rainfall-induced landslides based on displacement data.
  • Analyzed creeping landslides in Zigui County of the Three Gorges Reservoir area.
  • Developed a displacement ratio model using cumulative rainfall data.
  • Established a time-phased exponential warning model with four-level thresholds.
  • Validated the model with historical rainfall events and landslide data from 2021.
  • Model explains 30-40% of variation in landslide displacement based on cumulative rainfall.
  • Achieved 100% recall in issuing warnings for tested landslide events.
  • Reported an 8% false alarm rate for the warning system.

Abstract

Rainfall-induced landslides are characterized by stochasticity and complexity, making the development of effective early warning models crucial for disaster prevention and mitigation. Focusing on creeping landslides in Zigui County within the Three Gorges Reservoir area, this study proposes a rainfall early warning method based on a displacement ratio model. By analyzing the response relationship between landslide displacement and cumulative rainfall, a time-phased exponential early warning model was established, along with four-level warning thresholds (78.3 mm, 160.1 mm, and 196.6 mm). The model, which uses cumulative rainfall as the core input variable, explains approximately 30-40% of the variation in landslide displacement. Validation using three landslide events in 2021 and historical extreme rainfall events shows that the model successfully issued warnings for all incidents (100% recall), albeit with an 8% false alarm rate. The proposed method features concise parameters and strong operability, offering a technical reference for landslide early warning in similar regions, though its applicability should be further validated in conjunction with specific geological conditions.

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Cite This Study

Xing et al. (2026) studied this question.

synapsesocial.com/papers/69af95de70916d39fea4def2https://doi.org/10.1038/s41598-026-42802-7
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