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.

Viewed

Total article views: 1,781 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,502 252 27 1,781 18 16
  • HTML: 1,502
  • PDF: 252
  • XML: 27
  • Total: 1,781
  • BibTeX: 18
  • EndNote: 16
Views and downloads (calculated since 04 Jan 2022)
Cumulative views and downloads (calculated since 04 Jan 2022)

Viewed (geographical distribution)

Total article views: 1,781 (including HTML, PDF, and XML) Thereof 1,646 with geography defined and 135 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Jan 2023
Download
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.