Meteorological Hazard Assessment for Risk Early Warning of Geohazards Based on Causal-Heuristic Coupling: A Case Study of Nujiang Prefecture, China

Traditional hazard assessment often relies on simple spatial overlays of static susceptibility and dynamic rainfall thresholds, ignoring the spatial heterogeneity of geological responses to rainfall. To address this limitation, this study proposes a meteorological hazard assessment and dynamic early warning framework for Nujiang Prefecture, Yunnan Province, driven by heuristic spatial heterogeneity weighting. Specifically, this study introduces an S-T-C (Susceptibility–Rainfall trigger index–Causal-heuristic modulation factor) coupling framework that employs Causal Forest Double Machine Learning (CF-DML) to approximate conditional rainfall–geohazard associations and integrates them with static susceptibility and dynamic rainfall threshold modeling for warning zoning. The framework combines an antecedent effective rainfall threshold model (Antecedent Effective Rainfall Intensity–Duration–Daily Rainfall, EI-D-R) with an AutoGluon-based susceptibility model (AUC = 0.9178), and uses CF-DML as a flexible non-linear estimator to approximate conditional responses modulated by environmental factors. This heuristic adjustment is then incorporated into the S-T-C coupling framework. A retrospective case application shows that at the same observed Probability of Detection (POD = 76.5%), the S-T-C framework produces a more spatially selective warning footprint compared to traditional S-T product coupling, with very low-hazard zones expanding from 32.67% to 70.64% (a relative increase of 116.19%). Rather than claiming validated causal identification, this work illustrates that using CF-DML as an association-based, heuristic spatial-heterogeneity weighting approach can provide a more spatially explicit and retrospectively evaluated basis for geohazard assessment and regional risk management, subject to the data and modeling limitations discussed.