Scalable coastal inundation mapping using machine learning (Papers Track)

Ophelie Meuriot (IBM Research Europe); Anne Jones (IBM Research)

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Abstract

Coastal flooding is a significant climate hazard with impacts across economic sectors and society. This study provides a proof of concept for data-driven models for coastal flood inundation at the country scale, incorporating storm dynamics and geospatial characteristics to improve upon simpler geomorphological models. The best fit machine learning model scores an AUC of 0.92 in predicting flooded locations. For a case study storm event in December 2013 we find that all models over-predict flood extents, but that the machine learning model extents were closest to those observed.