Paper Information

Journal:   ARMANSHAHR   SPRING-SUMMER 2011 , Volume 4 , Number 6; Page(s) 63 To 74.
 
Paper: 

DEVELOPING AN EXPERT SYSTEM MODEL FOR PREDICTING ANNUAL URBAN RESIDENTIAL WATER DEMAND USING ARTIFICIAL NEURAL NETWORK (CASE STUDY: ILAM CITY)

 
 
Author(s):  TAGHVAE ALIAKBAR, POURJAFAR MOHAMADREZA, HOSSEINABADI MOSTAFA, RIYAHIMODVAR HOSSEIN
 
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Abstract: 

Water supply and its management for cities have been the main concerns of urban mangers and planners. The recognition of the amount of water demand and its related effective factors are treated as the important parameters in managing and controlling of urban water consumption. In this study, an expert model for predicting annual water demand using Artificial Neural Network (ANN) has been developed for water supply of the City of Ilam. The input parameters of the model as predictive variables are: Annual Income(X1), Consumption Zone(X2), Area(X3), building area(X4), Family size(X5), Number of valves(X6), and Annual water price(X7). The output parameter is Annual Urban Residential Water Demand (AURWD). By using the collected data and their preprocessing, an optimum structure of the expert model was derived. This was composed of: 3 layers with 7 neurons in the internal level, 10 neurons in the hidden level, and one node as the external layer in which Tansing activating function has been utilized. The comparisons of quantitative and qualitative results of the expert model with the observed quantities based on statistical criteria R2, RMSE and graphical tests indicate that ANN has been effectively implicated in AURWD prediction. Also by using the effective factors, a Multi Linear Regression (MLR) equation for AURWD has been developed. Comparisons of AURWD-MLR equation results with the expert model of AURWD-ANN show the superiority of expert system based on ANN to MLR equation. Therefore, the developed expert model in this study, AURWD-ANN, can be used as an expert decision support system by urban managers and planners.

 
Keyword(s): EXPERT SYSTEM, URBAN WATER DEMAND, EFFECTIVE FACTORS, ARTIFICIAL NEURAL NETWORK, ILAM CITY
 
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