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.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Joko Sampurno on behalf of the Authors (28 May 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (09 Jun 2022) by Stefano Pierini
RR by Anonymous Referee #1 (20 Jun 2022)
RR by Anonymous Referee #2 (26 Jun 2022)
ED: Publish subject to minor revisions (review by editor) (27 Jun 2022) by Stefano Pierini
AR by Joko Sampurno on behalf of the Authors (04 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (04 Jul 2022) by Stefano Pierini
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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.