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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.

Liu Y, Xi DG, Li ZL - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.

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.

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Related in: MedlinePlus

Sensitivity of the predictor to meteorological variables.Bars indicate changes as described for Fig. 4. P, daily average air pressure; Ta, average air temperature; WS, wind speed; SD, sunshine duration; R, total radiation.
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pone.0119082.g005: Sensitivity of the predictor to meteorological variables.Bars indicate changes as described for Fig. 4. P, daily average air pressure; Ta, average air temperature; WS, wind speed; SD, sunshine duration; R, total radiation.

Mentions: The sensitivity of the Chl-a predictor to meteorological variables is shown in Fig. 5. The sensitivity decreased as follows: P > WS, SD, and R > Ta.


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.

Liu Y, Xi DG, Li ZL - PLoS ONE (2015)

Sensitivity of the predictor to meteorological variables.Bars indicate changes as described for Fig. 4. P, daily average air pressure; Ta, average air temperature; WS, wind speed; SD, sunshine duration; R, total radiation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4359150&req=5

pone.0119082.g005: Sensitivity of the predictor to meteorological variables.Bars indicate changes as described for Fig. 4. P, daily average air pressure; Ta, average air temperature; WS, wind speed; SD, sunshine duration; R, total radiation.
Mentions: The sensitivity of the Chl-a predictor to meteorological variables is shown in Fig. 5. The sensitivity decreased as follows: P > WS, SD, and R > Ta.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China.

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.

Show MeSH
Related in: MedlinePlus