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

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

doi:https://doi.org/10.5194/npg-29-301-2022

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.

https://doi.org/10.5194/npg-29-301-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

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

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.