Forecasting of Urban Demand for Water in Tehran Using Structural, Time Series and GMDH Neural Networks Models: A Comparative Study

Conventionally, regression and time series analyses have been employed in modeling water demand forecasts. In recent years, the relatively new technique of neural networks (NNS) has been proposed as an efficient tool for modeling and forecasting. The objective of this study is to investigate the relatively new technique of GMDH – Type neural networks for the use of forecasting long – term urban water demand in Tehran city. The data employed in this study includes water consumption (per capita), water price, average household income and the annual average air temperature for the city of Tehran, Iran. The neural networks model, regression model, and time series model have been estimated and compared. The comparison reveals that the neural networks model consistently outperformed the regression and time series models developed in this study.