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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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Scatter plots of the observed data vs. the model predictions using different inputs.(A) Chl-a prediction with different variables used as inputs for training; (B) is the same as (A) but for validation; (C) is the same as (A) but for testing.
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pone.0119082.g003: Scatter plots of the observed data vs. the model predictions using different inputs.(A) Chl-a prediction with different variables used as inputs for training; (B) is the same as (A) but for validation; (C) is the same as (A) but for testing.

Mentions: Fig. 3 and Table 5 show the results of the three ANN models with different inputs of water quality and meteorological variables. The model with only meteorological factors (MF) as inputs always overestimated the concentration of Chl-a, whereas the model with only water quality variables (WQ) as inputs underestimated Chl-a, which was evident during the training period. Combining the water quality and meteorological variables (WF) improved the performance of the Chl-a predictor greatly by accurately detecting peak timing and magnitude. For example, the Corr of the WF model was 0.880, whereas the Corr of the WQ and MF models was only 0.574 and 0.686, respectively. The NSE of the WF model was 0.754, whereas the NSE of the WQ and MF models was 0.225 and 0.662, respectively.


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)

Scatter plots of the observed data vs. the model predictions using different inputs.(A) Chl-a prediction with different variables used as inputs for training; (B) is the same as (A) but for validation; (C) is the same as (A) but for testing.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0119082.g003: Scatter plots of the observed data vs. the model predictions using different inputs.(A) Chl-a prediction with different variables used as inputs for training; (B) is the same as (A) but for validation; (C) is the same as (A) but for testing.
Mentions: Fig. 3 and Table 5 show the results of the three ANN models with different inputs of water quality and meteorological variables. The model with only meteorological factors (MF) as inputs always overestimated the concentration of Chl-a, whereas the model with only water quality variables (WQ) as inputs underestimated Chl-a, which was evident during the training period. Combining the water quality and meteorological variables (WF) improved the performance of the Chl-a predictor greatly by accurately detecting peak timing and magnitude. For example, the Corr of the WF model was 0.880, whereas the Corr of the WQ and MF models was only 0.574 and 0.686, respectively. The NSE of the WF model was 0.754, whereas the NSE of the WQ and MF models was 0.225 and 0.662, respectively.

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