An Estimating Model for Water Quality of River Ganga using Artificial Neural Network
Yamini Soni1, Vikas Sejwar2

1Yamini Soni, Department of CSE & IT, Madhav Institute of Technology & Science, Gwalior (M.P), India.
2Vikas Sejwar, Department of CSE & IT, Madhav Institute of Technology & Science, Gwalior (M.P), India.

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1448-1453 | Volume-8 Issue-9, July 2019 | Retrieval Number I7900078919/19©BEIESP | DOI: 10.35940/ijitee.I7900.078919
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Abstract: In given propose paper we have worked on the water quality of river ganga, not only for management of water resources, but also for the prevention of water pollution, the water quality forecast has a more practical significance. To evolve suitable ideals for the water quality (WQ) in side the water physiques obtaining contaminant samples & then to confirm that these standards are encountered, this is the environmental WQ management program have goal. In the realistic standard setting, the institutional capacity of the basin’s water science, environmental, & the land usage situations, possible usages of getting water bodies, &the determination & implementation of WQ ideals has been kept in mind. In this paper, an efficient Machine learning algorithm was modeled. A feed forward error back propagation neural network is implemented with different training functions namely trainlm (Levenberg Marquardt back propagation), trainb (Batch training with weight & bias learning rules), trainr(Random order incremental training w/learning functions) & trainbr (Bayesian regularization). Five sampling stations along Ganga River stretch were selected from DEVPRAYAG-to-ROORKEE city inside the Uttarakhand state of the India. These states are Bihar, Uttarakhand, Delhi, UP& West Bengal. The hill rivers of the Uttarakhand are Alkananda, Bhagirathi, Mandakini. We have used the above given training functions at different-different learning rate (i.e., 0.01,0.03,0.05,0.07&0.09) to measure classification rate& use the mean square method to measure the performance of the model. Results indicate that the proposed algorithm gives best estimating model and generate less mean square error (i.e., 0.004)and accuracy is 99.5% at 0.09 learning rate with trainbr training functionin respect to other training function.
Keywords: Artificial Neural Network, Mean Squared Error Training Functions, Water Quality, Prediction of Water Quality.

Scope of the Article: Advanced Computer Networking