DAILY PAPER REVIEW

1108_Artificial neural network modeling of the river water quality- a case study

 
 
ESEL Paper Review_20131108
By Hong Guo
Mail: hongguo@gist.ac.kr
Phone: (+82) (0)10 82276568
1, Title and Author
Title: Artificial neural network modeling of the river water quality- a case study
Journal: Ecological modeling
Authors:
Kunwar P. Singh*, Ankita Basant1, Amrita Malik, Gunja Jain
Environmental Chemistry Division, Indian Institute of Toxicology Research, Post Box 80, MG Marg, Lucknow 226 001, India
2. Summary of Paper
? Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.
? The present study shows that the optimal networks are capable to capture long-term trends observed for the tedious water quality variables (DO and BOD), both in time and space. We propose the neural networks as effective tool for the computation of river water quality and it could also be used in other areas to improve the understanding of river pollution trends. The ANN can be seen to be a powerful predictive alternative to traditional modeling techniques.
3. Results
? Artificial neural network
? Different ANN models were constructed and tested in order to determine the optimum number of nodes in the hidden layer and transfer functions. Selection of an appropriate number of nodes in the hidden layer is very important aspect as a larger number of these may result in over-fitting. While a smaller number of nodes may not capture the information adequately.
? Fletcher and Goss (1993) suggested that the appropriate number of nodes in a hidden layer ranges from (2n1/2 +m) to (2n+ 1), where n is the number of input nodes and m is the number of output nodes.
? The network was trained using the training data set, and then it was validated with the validation data set. The optimal network size was selected from the one which resulted in minimum mean square error (MSE) in training and validation data sets.
? In the residuals figure. If the residuals appear to behave randomly it suggests that the model fits the data well. On the other hand, if nonrandom distribution is evident in the residuals, the model does not fit the data adequately.
? The sensitivity analysis results revealed that COD was the most effective input variables in the model
? A relatively low correlation (0.70?0.85) between the measured and model computed output variables (DO and BOD) in the present study may be due to the non-homogenous nature of the water quality (input and output) variables as these were measured over a long period of 10 years and at sampling sites distributed over a large geographical area. Moreover, relatively higher correlations between measured and model (NN) computed values of DO and BOD in various aquatic systems (Sengorur et al., 2006; Soyupak et al., 2003; Ying et al., 2007; Dogan et al., 2009) may be attributed to the relatively smaller data sets (samples) as well as limited number of the input variables used.
4. Contribution:
This research would be fully studied and applied to the research of the modeling for the food supernatant assessment
5. Contact (Mail address): kpsingh 52@yahoo.com,

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