Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series

Accurate water demand modeling for the city is very important for forecasting and policies adoption related to water resources management. Thus, for future requirements of water estimation, forecasting and modeling, it is important to utilize models with little errors. Water has a special place among the basic human needs, because it not hampers human life. The importance of the issue of water management in the extraction and consumption, it is necessary as a basic need. Municipal water applications is include a variety of water demand for domestic, public, industrial and commercial. Predicting the impact of urban water demand in better planning of water resources in arid and semiarid regions are faced with water restrictions.

One of the most important factors affecting the changing technological advances in production and demand functions, we must pay special attention to the layout pattern. Technology development is concerned not only technically, but also other aspects such as personal, non-economic factors (population, geographical and social factors) can be analyzed. Model examined in this study, a regression model is composed of a series of structural components over time allows changed invisible accidentally. Explanatory variables technology (both crystalline and amorphous) in a model according to which the material is said to be better, but because of the lack of measured variables over time can not be entered in the template. Model examined in this study, a regression model is composed of a series of structural component invisible accidentally changed over time allows. In this study, structural time series (STSM) and ARMA time series models have been used to model and estimate the water demand in Isfahan. Moreover, in order to find the efficient procedure, both models have been compared to each other. The desired data in this research include water consumption in Isfahan, water price and the monthly pay costs of water subscribers between 1388 and 1390. In structural time series model, the model was generated by entering the invisibility part of the process and development of a state-space model, as well as using maximum likelihood method and the Kalman-Filter algorithm.

Given the value of the test statistic ADF, with the exception of changing water use variables with a time difference of the steady rest. Superpopulation different modes of behavior were assessed based on the demand for water. Due to the likelihood ratio statistic is most suitable for the parameters, was diagnosed the steady-state level of randomness and the slope. Price and income elasticities of demand for water, respectively -0.81 and 0.85 shows that water demand is inelastic with respect to price and income and a lot of water is essential. Identify the nature of the request of one of the most important results in estimated water demand in the urban part of the state space time series structure and patterning methods, as an Alternative for variable is Technology preferences use. The model is estimated for the city's water demand time series model, respectively ARMA (3,1). Model performance metrics to compare the structural time series and time series ARMA, the result represents a structural time series model based on the fact that all the performance criteria in this study outperformed the ARMA model to forecast water city demand in the Isfahan.

Of a time series model structure to model ARMA in this research is to estimate the model and predict the number the less time is required, and also can be used for modeling of other variables (such as income and price) to this is helping to improve the models. Also, in ARMA time series the best model for data was selected according to the Schwarz Bayesian and Akaike criterion. Results indicate that the estimation of water demand using structural time series method is more efficient than when ARMA time series model is applied. Therefore, structural time series model can be used as an efficient tool for managers and planners in the Management Departmentsin order to forecast water demand. Used was for compare the performance of these two models of standard root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).