The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT
Transactions ID Number: 28-191
Full Name: Niaz ahmed Memon
Position: Assistant Professor
Age: ON
Sex: Male
Address: Department of Civil Engineering, Quaid-e-Awam University of Engineering Science & Technology Nawabshah
Country: PAKISTAN
Tel: +923332850220
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E-mail address: niaz59@hotmail.com
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Title of the Paper: Prediction of parametric value of Drinking water of Hyderabad city by Artificial neural network modeling
Authors as they appear in the Paper: Niaz ahmed Memon, M.A. Unar, A.K. Ansari, G.B. Khaskheli, B.A. Memon
Email addresses of all the authors: niaz59@hotmail.com,mukhtiar_unar@yahoo.com, qakpk@yahoo.com,gbk_60@hotmail.com ,drbashir@quest.edu.pk
Number of paper pages: 10
Abstract: In order to ascertain the quality of drinking water of the city of Hyderabad one of the significant parametric values of the drinking water was predicted. Like other parameters Electrical Conductivity (EC) is also imperative. The determination of electrical conductivity provides a prompt and expedient way to measure the accessibility of electrolytes in the water. There are swayed health effects on human life through these electrolytes, like disorder of salt and water balance in infants, heart patients, individuals with high blood pressure, and renal diseases. Salty taste is one of the aesthetic effects of EC if it exceeds 150 mS/m and if greater than 300 mS/m it does not slake the thirst. The drinking water supplied to Hyderabad city is taken from River Indus and the EC of this river remains questionable. The values of EC in drinking water of Hyderabad at selected locations were recorded. From 49 samples, the average values ranged from 658 to762. In order to deter!
mine the optimal value of EC with in the distribution system, where it deteriorates, it is necessary to predict it at different locations. The use of conventional methods to predict parametric values in the distribution systems is suffered from certain precincts. To get better drinking water quality by tumbling operational costs, Advance process control and automation technologies are the tools to be used normally. The application of Artificial Neural Networks in Water Supply Engineering is enticing and more accepted because of its high predictive accuracy. In this paper Radial Basis Neural Network has been demonstrated. The data sets were prepared for training the model. It was observed that the model has high predictive potentiality to predict the values of Electrical conductivity at 07 locations of distribution system of water supply in Hyderabad city. The removal of noisy and uninformative input variables from the data improved the efficiency of the network.
Keywords: Electrical Conductivity, Drinking water, Distribution System, ANNs, RBF, modeling, prediction
EXTENSION of the file: .doc
Special (Invited) Session: Predictive Potentiality of Artificial Neural Networks for predicting the Electrical conductivity (EC) of drinking water of Hyderabad city
Organizer of the Session: 591-565
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