Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system
Published in Environmental Modeling and Software, 2023
Recommended citation: Kwon, D. H., Hong, S. M., Abbas, A., Pyo, J., Lee, H. K., Baek, S. S., & Cho, K. H. (2023). Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning. Environmental Engineering Research, 28(1). https://doi.org/10.1016/j.envsoft.2023.105805
This paper uses re-inforcement learning to dynamically optimize parameters of hydrodynamic model (EFDC.)