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Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir, China

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  • Yu Liu
  • Du-Gang Xi
  • Zhao-Liang Li

Abstract

Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle. Subsequently, three predictors with different input variables were developed to examine the feasibility of incorporating meteorological factors into Chl-a prediction, which usually only uses water quality data. Finally, a sensitivity analysis was performed to examine how the Chl-a predictor reacts to changes in input variables. The results were as follows: first, ANN is a powerful predictive alternative to the traditional modeling techniques used for Chl-a prediction. The back program (BP) model yields slightly better results than all other ANNs, with the normalized mean square error (NMSE), the correlation coefficient (Corr), and the Nash-Sutcliffe coefficient of efficiency (NSE) at 0.003 mg/l, 0.880 and 0.754, respectively, in the testing period. Second, the incorporation of meteorological data greatly improved Chl-a prediction compared to models solely using water quality factors or meteorological data; the correlation coefficient increased from 0.574-0.686 to 0.880 when meteorological data were included. Finally, the Chl-a predictor is more sensitive to air pressure and pH compared to other water quality and meteorological variables.

Suggested Citation

  • Yu Liu & Du-Gang Xi & Zhao-Liang Li, 2015. "Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir, China," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-16, March.
  • Handle: RePEc:plo:pone00:0119082
    DOI: 10.1371/journal.pone.0119082
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    References listed on IDEAS

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    1. Onderka, Milan, 2007. "Correlations between several environmental factors affecting the bloom events of cyanobacteria in Liptovska Mara reservoir (Slovakia)—A simple regression model," Ecological Modelling, Elsevier, vol. 209(2), pages 412-416.
    2. Liu, Yong & Guo, Huaicheng & Yang, Pingjian, 2010. "Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis," Ecological Modelling, Elsevier, vol. 221(4), pages 681-688.
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    1. Kim, Hyo Gyeom & Hong, Sungwon & Jeong, Kwang-Seuk & Kim, Dong-Kyun & Joo, Gea-Jae, 2019. "Determination of sensitive variables regardless of hydrological alteration in artificial neural network model of chlorophyll a: Case study of Nakdong River," Ecological Modelling, Elsevier, vol. 398(C), pages 67-76.

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