Journal article
A Data-Driven Framework for Flood Mitigation: Transformer-Based Damage Prediction and Reinforcement Learning for Reservoir Operations
Water (Basel), Vol.17(20), 3024
10/21/2025
DOI: 10.3390/w17203024
Abstract
Floods are among the most destructive natural hazards, with damages expected to intensify under climate change and socio-economic pressures. Effective reservoir operation remains a critical yet challenging strategy for mitigating downstream impacts, as operators must navigate nonlinear system dynamics, uncertain inflow forecasts, and trade-offs between competing objectives. This study proposes a novel end-to-end data-driven framework that integrates process-based hydraulic simulations, a Transformer-based surrogate model for flood damage prediction, and reinforcement learning (RL) for reservoir gate operation optimization. The framework is demonstrated using the Coralville Reservoir (Iowa, USA) and two major historical flood events (2008 and 2013). Hydraulic and impact simulations with HEC-RAS and HEC-FIA were used to generate training data, enabling the development of a Transformer model that accurately predicts time-varying flood damages. This surrogate is coupled with a Transformer-enhanced Deep Q-Network (DQN) to derive adaptive gate operation strategies. Results show that the RL-derived optimal policy reduces both peak and time-integrated damages compared to expert and zero-opening benchmarks, while maintaining smooth and feasible operations. Comparative analysis with a genetic algorithm (GA) highlights the robustness of the RL framework, particularly its ability to generalize across uncertain inflows and varying initial storage conditions. Importantly, the adaptive RL policy trained on perturbed synthetic inflows transferred effectively to the hydrologically distinct 2013 event, and fine-tuning achieved near-identical performance to the event-specific optimal policy. These findings highlight the capability of the proposed framework to provide adaptive, transferable, and computationally efficient tools for flood-resilient reservoir operation.
Details
- Title: Subtitle
- A Data-Driven Framework for Flood Mitigation: Transformer-Based Damage Prediction and Reinforcement Learning for Reservoir Operations
- Creators
- Soheyla Tofighi - University of IowaFaruk GurbuzRicardo Mantilla - University of ManitobaShaoping Xiao - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Water (Basel), Vol.17(20), 3024
- DOI
- 10.3390/w17203024
- ISSN
- 2073-4441
- eISSN
- 2073-4441
- Publisher
- MDPI
- Grant note
- National Science foundation: 2226936 U.S. Department of Education: ED#P116S210005
This research was funded by the U.S. Department of Education under Grant Number ED#P116S210005 and the National Science Foundation under Grant Number 2226936. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Education and the National Science Foundation.
- Language
- English
- Date published
- 10/21/2025
- Academic Unit
- Iowa Technology Institute; Mechanical Engineering
- Record Identifier
- 9985017439102771
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