Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system

Abstract

Cyanobacterial blooms cause critical damage to aquatic ecosystems and water resources. Therefore, numerical models have been utilized to simulate cyanobacteria by calibrating model parameters for accurate simulation. While conventional calibration, which uses fixed water quality parameters throughout the simulation period, is commonly utilized, it may lead to inaccurate modeling results. To address it, this study proposed a reinforcement learning and environmental fluid dynamics code (EFDC-RL) model that uses real-time pontoon monitoring data and hyperspectral images to autonomously control water quality parameters. The EFDC-RL model showed impressive performance, with an R2 value of 0.7406 and 0.4126 for the training and test datasets, respectively. In comparison, the Chlorophyll-a simulation of conventional calibration had an R2 of 0.2133 and 0.0220, respectively. This study shows that the EFDC-RL model is a suitable framework for autonomous calibration of water quality parameters and real-time spatiotemporal simulation of cyanobacteria distribution.