A spatially adaptive fusion and ensemble learning workflow using early fusion, late fusion, and mixture of experts improves joint flood-landslide susceptibility mapping while capturing spatial heterogeneity.
Susceptibility assessment of multi-hazards using random forest—back propagation neural network coupling model: a Hangzhou city case study.Scientific Reports, 14(1):21783
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FL-MHSM: Spatially-adaptive Fusion and Ensemble Learning for Flood-Landslide Multi-Hazard Susceptibility Mapping at Regional Scale
A spatially adaptive fusion and ensemble learning workflow using early fusion, late fusion, and mixture of experts improves joint flood-landslide susceptibility mapping while capturing spatial heterogeneity.