Multi-Hazard Coastal Susceptibility Mapping Using Machine Learning and Deep Learning in Deltaic Louisiana

Abstract

Compound coastal hazards such as flooding, land subsidence, storm surge, and salinity intrusion impose accelerating risks on deltaic communities. This study presents a unified multi-hazard susceptibility mapping framework for Terrebonne Parish, Louisiana, modeling all four hazards from a common 30 m predictor stack, with per-hazard exclusion of label-related predictors. Eight Machine Learning and Deep Learning algorithms were benchmarked per hazard against an ensemble meta-learner. Generalizability was assessed under three designs of increasing spatial rigor: blocked holdout, interleaved block cross-validation, and a strict contiguous-zone design with a 5 km buffer. Best holdout F1-macro ranged from 0.644 (salinity) to 0.923 (flood). Interleaved-block cross-validation was statistically indistinguishable from holdout; only the buffered contiguous-zone design revealed genuine transfer limits, with F1-macro declining 12–54 percentage points by hazard. Ensemble stacking did not improve upon cross-validation-guided single-model selection despite roughly five times the training cost. Salinity labels were derived from 21 kriged monitoring stations (RMSE = 3.40 PSU; R2 = 0.82). A composite Multi-Hazard Susceptibility Index (mean = 0.675 parish-wide; 0.674 land-masked) identifies southern coastal Terrebonne as the priority zone for risk reduction, robust to reweighting of any single hazard. To our knowledge, this is the first framework to jointly map these four hazards while quantifying how validation design governs apparent model transferability.