Accelerating Compound Flood Risk Assessments Through Active Learning A After generating synthetic events from a statistical model fitted to the selected flood drivers, the proposed framework applies a treed gaussian process (tgp). a tgp uses active learning to explore the uncertainty associated with the response of damages to synthetic events. This study develops a conceptual framework that allows for a better representation of compound flood risk while limiting the increase in the overall computational time.
Schematic Of The Globally Applicable Compound Flood Risk Framework A study in charleston county, sc, applies active learning to accelerate and enhance compound flood risk assessments, reducing computation and improving damage accuracy by addressing multiple interacting flood drivers and sea level rise. To assess the effect of simplifications on current compound flood risk assessments, the flood risk associated with the damage of the complete model to the five testing event sets was modeled. Therefore, this study aims to explore active learning to improve the quantification of compound flood risk assessments while limiting the increase in overall computational time. To the best of my knowledge, this is the first study to apply active learning techniques in the context of compound flooding, demonstrating an approach to advancing this area of research.
Pdf Urban Flood Risk Assessment Through The Integration Of Natural Therefore, this study aims to explore active learning to improve the quantification of compound flood risk assessments while limiting the increase in overall computational time. To the best of my knowledge, this is the first study to apply active learning techniques in the context of compound flooding, demonstrating an approach to advancing this area of research.
Assessing Compound Coastal Fluvial Flood Impacts And Resilience Under
Pdf Understanding The Compound Flood Risk Along The Coast Of The
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