Articles | Volume 25, issue 4
https://doi.org/10.5194/nhess-25-1353-2025
https://doi.org/10.5194/nhess-25-1353-2025
Research article
 | 
09 Apr 2025
Research article |  | 09 Apr 2025

Accelerating compound flood risk assessments through active learning: A case study of Charleston County (USA)

Lucas Terlinden-Ruhl, Anaïs Couasnon, Dirk Eilander, Gijs G. Hendrickx, Patricia Mares-Nasarre, and José A. Á. Antolínez

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Anderson, D., Rueda, A., Cagigal, L., Antolinez, J. A. A., Mendez, F. J., and Ruggiero, P.: Time‐Varying Emulator for Short and Long‐Term Analysis of Coastal Flood Hazard Potential, J. Geophys. Res.-Oceans, 124, 9209–9234, https://doi.org/10.1029/2019jc015312, 2019. a
Antolínez, J. A. A., Méndez, F. J., Anderson, D., Ruggiero, P., and Kaminsky, G. M.: Predicting Climate‐Driven Coastlines With a Simple and Efficient Multiscale Model, J. Geophys. Res.-Earth Surface, 124, 1596–1624, https://doi.org/10.1029/2018jf004790, 2019. a
Apel, H., Martínez Trepat, O., Hung, N. N., Chinh, D. T., Merz, B., and Dung, N. V.: Combined fluvial and pluvial urban flood hazard analysis: concept development and application to Can Tho city, Mekong Delta, Vietnam, Nat. Hazards Earth Syst. Sci., 16, 941–961, https://doi.org/10.5194/nhess-16-941-2016, 2016. a, b
Arns, A., Wahl, T., Haigh, I., Jensen, J., and Pattiaratchi, C.: Estimating extreme water level probabilities: A comparison of the direct methods and recommendations for best practise, Coast. Eng., 81, 51–66, https://doi.org/10.1016/j.coastaleng.2013.07.003, 2013. a
Bakker, T. M., Antolínez, J. A., Leijnse, T. W., Pearson, S. G., and Giardino, A.: Estimating tropical cyclone-induced wind, waves, and surge: A general methodology based on representative tracks, Coast. Eng., 176, 104154, https://doi.org/10.1016/j.coastaleng.2022.104154, 2022. a, b
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This study develops a conceptual framework that uses active learning to accelerate compound flood risk assessments. A case study of Charleston County shows that the framework achieves faster and more accurate risk quantification compared to the state-of-the-art. This win–win allows for an increase in the number of flooding parameters, which results in an 11.6 % difference in the expected annual damages. Therefore, this framework allows for more comprehensive compound flood risk assessments.
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