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

Seok Min Hong, Ather Abbas, Soobin Kim, Do Hyuck Kwon, Nakyung Yoon, Daeun Yun, Sanguk Lee, Yakov Pachepsky, Jong Cheol Pyo*, Kyung Hwa Cho*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Article number105805
JournalEnvironmental Modelling and Software
Volume168
DOIs
StatePublished - Oct 2023

Bibliographical note

Funding Information:
This work was supported by the ICT R&D program of MSIT/IITP ( 2018-0-00219 , Space-time complex artificial intelligence blue-green algae prediction technology based on direct-readable water quality complex sensor and hyperspectral image); and the Korea Environment Industry & Technology Institute (KEITI) through Aquatic Ecosystem Conservation Research Program of Korea Environment Industry & Technology Institute (KEITI) , funded by Korea Ministry of Environment (MOE) ( 2020003030003 ).

Publisher Copyright:
© 2023

Keywords

  • Autonomous calibration
  • Cyanobacteria
  • Environmental fluid dynamics code
  • Real-time monitoring
  • Reinforcement learning

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling

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