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Lookup NU author(s): Dr Manuel HerreraORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Flood-related disasters have become increasingly frequent in recent years, as evidenced by severe events in Brazil, Spain, and Germany. The development of impact-based flood early warning systems (IBFWS) has been an essential tool for minimizing human and economic losses. Recent advances in data-driven models show strong potential for improving flood forecasting capabilities. More promising approach that has emerged is the use of physics-enhanced machine learning models. These models incorporate physical and hydrological concepts into data-driven frameworks, which enhance their interpretability and robustness. This paper proposes a physics-enhanced Long Short-Term Memory (LSTM) model to incorporate inter-station lag times into the model’s feature selection and temporal configuration, improving flood forecasts. The framework is applied to a flood-prone urban basin using high-resolution (10-minute) rainfall and streamflow data, assessing both overall forecast skill and the accuracy of flood events, particularly the peak magnitude and timing errors. Results demonstrate that the physics-enhanced configuration consistently increases prediction accuracy by reducing redundancy among inputs. Moreover, it maintains the physical coherence of the hydrological processes, supporting the transition from black-box to grey-box modeling. The resulting architecture remained computationally efficient, highlighting the potential of physics-enhanced neural networks for operational and impact-based flood forecasting.
Author(s): Bezerra RPG, Eleutério JC, Solha PB, Brentan BM, de Mello CR, Herrera M, Rodrigues AF
Publication type: Article
Publication status: Published
Journal: Journal of Hydroinformatics
Year: 2026
Pages: epub ahead of print
Online publication date: 06/05/2026
Acceptance date: 20/04/2026
Date deposited: 22/04/2026
ISSN (print): 1464-7141
ISSN (electronic): 1465-1734
Publisher: IWA Publishing
URL: https://doi.org/10.2166/hydro.2026.180
DOI: 10.2166/hydro.2026.180
Data Access Statement: All relevant data are included in the paper or its Supplementary Information.
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