A multi-source landslide early warning model based on dynamic monitoring data and the SAAHP-FCE method - Duo Wang; Guanwen Huang; Qin Zhang; Yang Gao; Yuan Du; Xiaohuan Liu (Geomatics, Natural Hazards & Risk)
Landslide early warning is crucial for mitigating disaster impacts but remains challenging due to numerous influencing factors, resulting in untimely or unreliable warnings. To address these issues, a multi-source early warning model using dynamic monitoring data is proposed, combining the Self-Adaptive Analytic Hierarchy Process (SAAHP) with the Fuzzy Comprehensive Evaluation (FCE) method. First, a hierarchical structure and quantitative thresholds are established based on the landslide mechanism and monitoring data, classifying warnings into four levels: safety, caution, vigilance, and alarm. SAAHP is then applied to calculate evaluation factor weights using a judgment matrix. Finally, FCE evaluates the overall impact of these factors, providing warning levels, probabilities, and scores. An experiment on the Heifangtai DC#7 landslide demonstrated the model’s success in capturing all four events in this disaster. The ‘alarm’ warning was issued two days earlier than traditional velocity-based models. Similarly, for the Xinpu landslide in the Three Gorges Reservoir area, the model integrated multi-station monitoring data, reducing the impact of data interruptions on warning accuracy. This approach effectively identifies landslide risks in advance, providing comprehensive, dynamic warnings and overcoming the limitations of single-indicator models. It offers a reliable framework for improving early warning precision and disaster preparedness.