Articles | Volume 29, issue 3
Nonlin. Processes Geophys., 29, 301–315, 2022
https://doi.org/10.5194/npg-29-301-2022
Nonlin. Processes Geophys., 29, 301–315, 2022
https://doi.org/10.5194/npg-29-301-2022
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 et al.

Data sets

Dataset for Integrated hydrodynamic and machine learning models Joko Sampurno and Randy Ardianto https://doi.org/10.5281/zenodo.6795963

Data Pasang Surut Sungai Air Kapuas Pontianak Maritime Meteorological Station (PMMS) https://maritim.kalbar.bmkg.go.id/

Model code and software

R-Code for Integrated hydrodynamic and machine learning models Joko Sampurno https://doi.org/10.5281/zenodo.6795949

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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.