Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 26, issue 11
Articles | Volume 26, issue 11
https://doi.org/10.5194/nhess-26-5441-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-26-5441-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 11
Research article
 | 
02 Nov 2026
Research article |  | 02 Nov 2026

Reducing false alarms in crackmeter-based early warning of small-scale slope deformation via deep learning-driven asynchronous displacement–rainfall data fusion

Shubing Ouyang, Daichao Li, Minjiang Liu, Fengjian Ge, Xia Zheng, Yuan Li, and Sheng Wu

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Short summary
Crackmeter-based warning systems generate many false alarms, causing alarm fatigue. An AI model was developed to combine real-time displacement and rainfall data and distinguish genuine deformation warnings from false alarms caused by noise or other disturbances. It identified 89.38% of confirmed true warnings, and 90.32% of alerts classified as genuine were correct. Analyses of misclassified cases informed additional operational safeguards to further reduce missed warnings.
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