Critical threshold mining of landslide deformation and intelligent early-warning methods based on multi-factor fusion
Citation
Liu, D., Tang, D., Ma, J., Zhang, J., Tang, H., Leng, Y. (2024). Critical threshold mining of landslide deformation and intelligent early-warning methods based on multi-factor fusion. Bulletin of Engineering Geology and the Environment, 83: 352. Link to paper
Abstract
Deformation and development of landslides are highly complex processes and relying solely on a single monitoring factor is insufficient to accurately assess the entire evolutionary trend of landslide deformation. To construct a low-cost and widely applicable landslide warning model, this study installed two types of conventional monitoring devices on a landslide mass (displacement meters and rain gauges). The Saito method was applied to identify the macroscopic deformation stages of the landslide and to calculate the daily average deformation rates at each stage. Subsequently, a five-level warning pattern based on deformation rates was established and the critical thresholds for each warning level were determined. Using daily displacement and rainfall as well as bedrock hardness and slope as the factors, a feature vector set was constructed by associating the warning levels corresponding to the daily average deformation rates at each stage. An integrated machine learning network was employed to develop an intelligent landslide warning model.