Articles | Volume 20, issue 4
Nat. Hazards Earth Syst. Sci., 20, 1149–1161, 2020
https://doi.org/10.5194/nhess-20-1149-2020
Nat. Hazards Earth Syst. Sci., 20, 1149–1161, 2020
https://doi.org/10.5194/nhess-20-1149-2020

Invited perspectives 29 Apr 2020

Invited perspectives | 29 Apr 2020

Invited perspectives: How machine learning will change flood risk and impact assessment

Dennis Wagenaar et al.

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Cited articles

Aarthi, A. D. and Gnanappazham, L.: Comparison of Urban Growth Modeling Using Deep Belief and Neural Network Based Cellular Automata Model – A Case Study of Chennai Metropolitan Area, Tamil Nadu, India, Journal of Geographic Information System, 11, 1–16, 2019. 
Abrahart, R. J. and See, L. M.: Neural network modelling of non-linear hydrological relationships, Hydrol. Earth Syst. Sci., 11, 1563–1579, https://doi.org/10.5194/hess-11-1563-2007, 2007. 
Alshehhi, R., Marpu, P. R., Woon, W., and Dalla Maru, M.: Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks, ISPRS J. Photogramm., 130, 139–149, 2017. 
Amadio, M., Scorzini, A. R., Carisi, F., Essenfelder, A. H., Domeneghetti, A., Mysiak, J., and Castellarin, A.: Testing empirical and synthetic flood damage models: the case of Italy, Nat. Hazards Earth Syst. Sci., 19, 661–678, https://doi.org/10.5194/nhess-19-661-2019, 2019. 
Ames, M. G.: Deconstructing the algorithmic sublime, Big Data & Society, 5, 1–4, https://doi.org/10.1177/2053951718779194, 2018. 
Short summary
This invited perspective paper addresses how machine learning may change flood risk and impact assessments. It goes through different modelling components and provides an analysis of how current assessments are done without machine learning, current applications of machine learning and potential future improvements. It is based on a 2-week-long intensive collaboration among experts from around the world during the Understanding Risk Field lab on urban flooding in June 2019.
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