Articles | Volume 29, issue 3
https://doi.org/10.5194/npg-29-301-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-29-301-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Aug 2022
Research article |
| 01 Aug 2022
| 01 Aug 2022
Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta
Joko Sampurno
CORRESPONDING AUTHOR
Earth and Life Institute (ELI), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Department of Physics, Fakultas MIPA, Universitas Tanjungpura, Pontianak, 78124, Indonesia
Valentin Vallaeys
Earth and Life Institute (ELI), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Randy Ardianto
Pontianak Maritime Meteorological Station, Pontianak, 78111, Indonesia
Emmanuel Hanert
Earth and Life Institute (ELI), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
Institute of Mechanics, Materials and Civil Engineering (iMMC), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, 1348, Belgium
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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.
In this study, we successfully built and evaluated machine learning models for predicting water...
