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Article

Predicting BOD under Various Hydrological Conditions in the Dongjin River Basin Using Physics-Based and Data-Driven Models

by 1 and 2,*
1
Department of Urban Planning and Real Estate, Cheongju University, 298 Daeseongro, Cheongwon-gu, Cheongju 28503, Chungbuk, Korea
2
Department of Environment Engineering, Cheongju University, 298 Daeseongro, Cheongwon-gu, Cheongju 28503, Chungbuk, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: George Arhonditsis
Water 2021, 13(10), 1383; https://doi.org/10.3390/w13101383
Received: 29 March 2021 / Revised: 29 April 2021 / Accepted: 15 May 2021 / Published: 16 May 2021
(This article belongs to the Section Water Resources Management, Policy and Governance)
The water quality of the Dongjin River deteriorates during the irrigation period because the supply of river maintenance water to the main river is cut off by the mass intake of agricultural weirs located in the midstream regions. A physics-based model and a data-driven model were used to predict the water quality in the Dongjin River under various hydrological conditions. The Hydrological Simulation Program–Fortran (HSPF), which is a physics-based model, was constructed to simulate the biological oxygen demand (BOD) in the Dongjin River Basin. A Gamma Test was used to derive the optimal combinations of the observed variables, including external water inflow, water intake, rainfall, and flow rate, for irrigation and non-irrigation periods. A data-driven adaptive neuro-fuzzy inference system (ANFIS) model was then built using these results. The ANFIS model built in this study was capable of predicting the BOD from the observed hydrological data in the irrigation and non-irrigation periods, without running the physics-based model. The predicted results have high confidence levels when compared with the observed data. Thus, the proposed method can be used for the reliable and rapid prediction of water quality using only monitoring data as input. View Full-Text
Keywords: data-driven model; HSPF model; ANFIS; BOD; Water quality prediction data-driven model; HSPF model; ANFIS; BOD; Water quality prediction
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MDPI and ACS Style

Lee, E.; Kim, T. Predicting BOD under Various Hydrological Conditions in the Dongjin River Basin Using Physics-Based and Data-Driven Models. Water 2021, 13, 1383. https://doi.org/10.3390/w13101383

AMA Style

Lee E, Kim T. Predicting BOD under Various Hydrological Conditions in the Dongjin River Basin Using Physics-Based and Data-Driven Models. Water. 2021; 13(10):1383. https://doi.org/10.3390/w13101383

Chicago/Turabian Style

Lee, Eunjeong, and Taegeun Kim. 2021. "Predicting BOD under Various Hydrological Conditions in the Dongjin River Basin Using Physics-Based and Data-Driven Models" Water 13, no. 10: 1383. https://doi.org/10.3390/w13101383

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