Deep reinforcement learning-based optimal weir operation to control cyanobacteria under extreme hydrological and temperature variations

초록

Cyanobacterial blooms threaten aquatic ecosystems and human health, and their occurrence has been intensified by hydrological and weather variations associated with climate change. This study proposes an optimal weir operation framework by coupling Long Short-Term Memory (LSTM) models with a Soft Actor-Critic (SAC)-based deep reinforcement learning (DRL) agent for managing cyanobacterial blooms. The LSTM models simulated hydrological, nutrient, and cyanobacteria-related state variables that were input to the DRL environment to update continuous weir operation. The SAC-based DRL system was trained to determine the optimal weir overflow strategy to reduce cyanobacteria compared to the baseline LSTM simulation within operational storage constraints. Additional sensitivity analyses indicated that average operation metrics were relatively stable under cyanobacteria prediction perturbations and across SAC training seeds, while cyanobacterial reduction performance and peak-event responses remained sensitive to these uncertainties. On the validation set, the LSTM models achieved R2 values ranging from 0.75 to 0.89 for hydrological and nutrient variables and 0.76 for cyanobacteria regression, while the cyanobacteria occurrence classification yielded an accuracy of 92.22%. The DRL-based optimal weir operation reduced cyanobacteria concentrations by up to 13.01% and 8.39% during training and validation, respectively. Hydro-thermal scenarios revealed that higher inflow can partially mitigate thermal stress on cyanobacteria; however, the tested inflow range had limited capacity to reduce cyanobacterial concentrations under rising water temperature conditions. These results demonstrated the potential ability of the proposed LSTM-DRL framework to manage cyanobacterial blooms, though site-specific calibration and further uncertainty evaluation are required before operational application.

키워드