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Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea
- Source :
- Science of The Total Environment. 502:31-41
- Publication Year :
- 2015
- Publisher :
- Elsevier BV, 2015.
-
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.
- Subjects :
- Chlorophyll
Engineering
Environmental Engineering
Fresh Water
Interval (mathematics)
Algal bloom
Artificial Intelligence
Republic of Korea
Water Pollution, Chemical
Environmental Chemistry
Sensitivity (control systems)
Waste Management and Disposal
Hydrology
Warning system
Artificial neural network
business.industry
Chlorophyll A
Pollution
Support vector machine
Models, Chemical
Neural Networks, Computer
Water quality
Estuaries
business
Eutrophication
Water Pollutants, Chemical
Environmental Monitoring
Subjects
Details
- ISSN :
- 00489697
- Volume :
- 502
- Database :
- OpenAIRE
- Journal :
- Science of The Total Environment
- Accession number :
- edsair.doi.dedup.....72a41e57d395974ace59f0694133e716