Limits...
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.

Show MeSH

Related in: MedlinePlus

Configuration of the Chlorophyll-a (Chl-a) predictor using artificial neural networks (ANN).Left: input water quality and meteorological variables extracted by the correlation coefficient threshold method; middle: configuration of the predictor; right: predicted Chl-a. White circles on the left and right represent input and output neurons, respectively. Black circles represent neurons in the hidden layer. Lines around the circles indicate the data flow. A total of 49 variables of 12 features (27 variables of 7 water quality features and 22 variables of 5 meteorological features) were used.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4359150&req=5

pone.0119082.g002: Configuration of the Chlorophyll-a (Chl-a) predictor using artificial neural networks (ANN).Left: input water quality and meteorological variables extracted by the correlation coefficient threshold method; middle: configuration of the predictor; right: predicted Chl-a. White circles on the left and right represent input and output neurons, respectively. Black circles represent neurons in the hidden layer. Lines around the circles indicate the data flow. A total of 49 variables of 12 features (27 variables of 7 water quality features and 22 variables of 5 meteorological features) were used.

Mentions: (3) Configuration. Based on the above parameters, the Chl-a predictor was designed as shown in Fig. 2. The predictor comprises three parts: an input layer, an output layer and several hidden layers. Each layer contains several neurons. Each neuron receives inputs from neurons in the previous layers or from external sources and then converts the inputs either to an output signal or to another input signal for neurons in the next layer. The connections between neurons in successive layers were assigned weighted values, which represent the importance of that connection in the network.


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)

Configuration of the Chlorophyll-a (Chl-a) predictor using artificial neural networks (ANN).Left: input water quality and meteorological variables extracted by the correlation coefficient threshold method; middle: configuration of the predictor; right: predicted Chl-a. White circles on the left and right represent input and output neurons, respectively. Black circles represent neurons in the hidden layer. Lines around the circles indicate the data flow. A total of 49 variables of 12 features (27 variables of 7 water quality features and 22 variables of 5 meteorological features) were used.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0119082.g002: Configuration of the Chlorophyll-a (Chl-a) predictor using artificial neural networks (ANN).Left: input water quality and meteorological variables extracted by the correlation coefficient threshold method; middle: configuration of the predictor; right: predicted Chl-a. White circles on the left and right represent input and output neurons, respectively. Black circles represent neurons in the hidden layer. Lines around the circles indicate the data flow. A total of 49 variables of 12 features (27 variables of 7 water quality features and 22 variables of 5 meteorological features) were used.
Mentions: (3) Configuration. Based on the above parameters, the Chl-a predictor was designed as shown in Fig. 2. The predictor comprises three parts: an input layer, an output layer and several hidden layers. Each layer contains several neurons. Each neuron receives inputs from neurons in the previous layers or from external sources and then converts the inputs either to an output signal or to another input signal for neurons in the next layer. The connections between neurons in successive layers were assigned weighted values, which represent the importance of that connection in the network.

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