AI based prediction of wastewater treatment plant effluent to supplement the minimal instream flow in the Han River

Kim, Jong Beom; Park, Seon Yeong; Singh, Vikash; Kim, Chang Gyun

doi:2025.30.6.250028

Environmental Engineering Research 2025;30(6): 250028 DOI: https://doi.org/10.4491/eer.2025.028

AI based prediction of wastewater treatment plant effluent to supplement the minimal instream flow in the Han River

Jong Beom Kim¹, Seon Yeong Park², Vikash Singh³, and Chang Gyun Kim^3,4

^†

¹Department of Ocean Sciences, INHA University, Incheon 22212, Republic of Korea
²Institute of Environmental Research, INHA University, Incheon 22212, Republic of Korea
³BK21 Four Convergence Program for Full Cycle Control of Microplastics, INHA University, Incheon 22212, Republic of Korea
⁴Department of Environmental Engineering, INHA University, Incheon 22212, Republic of Korea

Corresponding Author: Chang Gyun Kim ,Tel: +82 32-860-7561, Email: cgk@inha.ac.kr

Received: January 12, 2025; Accepted: March 9, 2025.

ABSTRACT

Securing the minimum instream flow is crucial for utilizing rivers as sustainable water resources and maintaining a resilient ecosystem. For this, the effluent discharged from the J wastewater treatment plant (WWTP) near Hangang Bridge on the Han River (Seoul, South Korea) has been predicted to monitor its contribution to the minimum instream flow using a nonlinear autoregressive exogenous (NARX) model and a support vector regression model using radial basis function kernel (SVR-RBF). Firstly, the discharge flow rate of J WWTP has been predicted based on the influent water quality parameters (i.e., BOD₅, COD (or TOC), TN, and TP) and local meteorological data (i.e., humidity and precipitation). Furthermore, parameters were attempted to be more accurately optimized by coupled with principal component analysis (PCA). Simulation without PCA indicated that SVR-RBF outperformed NARX, achieving superior accuracy with RMSE = 1.73%; MAE = 1.23%; and SCC = 0.53. Combining with PCA, both have improved their prediction accuracy higher than without PCA, where SVR-RBF still achieved greater accuracy than NARX. It is decided that the SVR-RBF coupling with PCA can be the most accurate way to predict the WWTP discharge and its influences on the minimum instream flow rate.