Articles | Volume 29, issue 3
Research article
01 Aug 2022
Research article |  | 01 Aug 2022

Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta

Joko Sampurno, Valentin Vallaeys, Randy Ardianto, and Emmanuel Hanert

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Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
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Cited articles

Alipour, A., Ahmadalipour, A., Abbaszadeh, P., and Moradkhani, H.: Leveraging machine learning for predicting flash flood damage in the Southeast US, Environ. Res. Lett., 15, 024011,, 2020. 
Assem, H., Ghariba, S., Makrai, G., Johnston, P., Gill, L., and Pilla, F.: Urban Water Flow and Water Level Prediction Based on Deep Learning, in: Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, Lecture Notes in Computer Science, Springer, Cham, 10536, 317–329,, 2017. 
BATimetri NASional:, last access: 14 July 2021. 
Bevacqua, E., Maraun, D., Vousdoukas, M. I., Voukouvalas, E., Vrac, M., Mentaschi, L., and Widmann, M.: Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change, Sci. Adv., 5, eaaw5531,, 2019. 
Bhaskaran, P. K., Gayathri, R., Murty, P. L. N., Bonthu, S. R., and Sen, D.: A numerical study of coastal inundation and its validation for Thane cyclone in the Bay of Bengal, Coast. Eng., 83, 108–118,, 2014. 
Short summary
In this study, we successfully built and evaluated machine learning models for predicting water level dynamics as a proxy for compound flooding hazards in a data-scarce delta. The issues that we tackled here are data scarcity and low computational resources for building flood forecasting models. The proposed approach is suitable for use by local water management agencies in developing countries that encounter these issues.