title: Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.
abstract: Chlorophyll-a (Chl-a) is a direct indicator used to evaluate the ecological state of a waterbody, such as algal blooms that degrade the water quality in lakes, reservoirs and estuaries. In this study, artificial neural network (ANN) and support vector machine (SVM) were used to predict Chl-a concentration for the early warning in the Juam Reservoir and Yeongsan Reservoir, which are located in an upstream region (freshwater reservoir) and downstream region (estuarine reservoir), respectively. Weekly water quality data and meteorological data for a 7-year period were used to train and validate both the ANN and SVM models. The Latin-hypercube one-factor-at-a-time (LH-OAT) method and a pattern search algorithm were applied to perform sensitivity analyses for the input variables and to optimize the parameters of the two models, respectively. Results revealed that the two models well-reproduced the temporal variation of Chl-a based on the weekly input variables. In particular, the SVM model showed better performance than the ANN model, displaying a higher prediction accuracy in the validation step. The Williams-Kloot test and sensitivity analysis demonstrated that the SVM model was superior for predicting Chl-a in terms of prediction accuracy and description of the cause-and-effect relationship between Chl-a concentration and environmental variables in both the Juam Reservoir and Yeongsan Reservoir. Furthermore, a 7-day interval was determined as an efficient early warning interval in the two reservoirs. As such, this study suggested an effective early-warning prediction method for Chl-a concentration and improved the eutrophication management scheme for reservoirs.
authors.affiliation: School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), 261 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of Korea.
authors.forename: Yongeun
authors.initials: Y
authors.lastname: Park
authors.affiliation: School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, Republic of Korea.
authors.forename: Kyung Hwa
authors.initials: KH
authors.lastname: Cho
authors.affiliation: School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), 261 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of Korea.
authors.forename: Jihwan
authors.initials: J
authors.lastname: Park
authors.affiliation: Jeollanam-do Environmental Industries Promotion Institute, 650-94 Songgye-ro, Seongjeon-myeon, Gangjin-gun, Jeollanam-do, 527-811, Republic of Korea.
authors.forename: Sung Min
authors.initials: SM
authors.lastname: Cha
authors.affiliation: School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), 261 Cheomdan-gwagiro, Buk-gu, Gwangju 500-712, Republic of Korea. Electronic address: joonkim@gist.ac.kr.
authors.forename: Joon Ha
authors.initials: JH
authors.lastname: Kim
mesh_terms: D001185:Artificial Intelligence; D002734:Chlorophyll / Q000032:analysis*; D000077194:Chlorophyll A; D004784:Environmental Monitoring / Q000379:methods*; D063366:Estuaries; D005618:Fresh Water / Q000737:chemistry; D008956:Models, Chemical; D016571:Neural Networks, Computer; D056910:Republic of Korea; D014874:Water Pollutants, Chemical / Q000032:analysis*; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data*
chemical_list: D014874:Water Pollutants, Chemical; D002734:Chlorophyll; D000077194:Chlorophyll A
keywords: Artificial neural network; Chlorophyll-a; Early warning; Prediction accuracy; Sensitivity analysis; Support vector machine