Operation rules for the Colônia River reservoir under extreme drought: Dynamic Programming and Artificial Neural Networks

Abstract

ABSTRACT This study developed an optimized operating rule for the Colônia River reservoir (BA) via a hybrid DP-ANN approach, aiming to mitigate regional water vulnerability. DP was applied to a 20-year historical series (2000-2020) to minimize supply deficits and ecological flow violations, with its operating policy emulated by an MLP neural network to ensure practical applicability. The model's effectiveness was validated in AcquaNet software, using the 2015-2016 extreme drought as a stress scenario across eight distinct water availability and initial storage configurations. Results demonstrate that the ANN Rule outperformed manual management, increasing ecological compliance from 58.3% to 95.8% in the most critical scenario, without compromising urban supply reliability. Achieving regional water resilience requires management that combines operational optimization, ecological integrity, and diversification of water sources.