MODELING AND FORECASTING OF WATER DEMAND IN ISFAHAN USING UNDERLYING TREND CONCEPT AND TIME SERIES

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SADEGHI H.; AKHOOND ALI A.M.; HADDAD M.; GOLABI M.R.

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Journal: Journal of Water and Soil Year:2015 | Volume:29 | Issue:2 Page(s): 251-262

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Title

MODELING AND FORECASTING OF WATER DEMAND IN ISFAHAN USING UNDERLYING TREND CONCEPT AND TIME SERIES

Author(s)

SADEGHI H. | AKHOOND ALI A.M. | HADDAD M. | GOLABI M.R. | Issue Writer Certificate

Keywords

ARMA TIME SERIESQ2

FORECASTING OF CITY WATERQ2

KALMAN FILTER ALGORITHMQ2

STRUCTURAL TIME SERIESQ2

UNDERLYING TRENDQ2

Abstract

Introduction: Accurate water demand modeling for the city is very important for forecasting and policiesadoption related to water resources management. Thus, for future requirements of water estimation, forecastingand modeling, it is important to utilize models with little errors. Water has a special place among the basichuman needs, because it not hampers human life. The importance of the issue of water management in theextraction and consumption, it is necessary as a basic need. Municipal water applications is include a variety ofwater demand for domestic, public, industrial and commercial. Predicting the impact of urban water demand inbetter planning of water resources in arid and semiarid regions are faced with water restrictions.Materials and Methods: One of the most important factors affecting the changing technological advances inproduction and demand functions, we must pay special attention to the layout pattern. Technology developmentis 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 composedof a series of structural components over time allows changed invisible accidentally. Explanatory variablestechnology (both crystalline and amorphous) in a model according to which the material is said to be better, butbecause of the lack of measured variables over time can not be entered in the template. Model examined in thisstudy, a regression model is composed of a series of structural component invisible accidentally changed overtime allows. In this study, STRUCTURAL TIME SERIES (STSM) and ARMA TIME SERIES models have been used to modeland estimate the water demand in Isfahan. Moreover, in order to find the efficient procedure, both models havebeen compared to each other. The desired data in this research include water consumption in Isfahan, water priceand the monthly pay costs of water subscribers between 1388 and 1390. In STRUCTURAL TIME SERIES model, themodel was generated by entering the invisibility part of the process and development of a state-space model, aswell as using maximum likelihood method and the Kalman-Filter algorithm.Results and Discussion: Given the value of the test statistic ADF, with the exception of changing water usevariables with a time difference of the steady rest. Superpopulation different modes of behavior were assessedbased on the demand for water. Due to the likelihood ratio statistic is most suitable for the parameters, wasdiagnosed 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 ofwater is essential. Identify the nature of the request of one of the most important results in estimated waterdemand in the urban part of the state space time series structure and patterning methods, as an Alternative forvariable 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 seriesARMA, the result represents a STRUCTURAL TIME SERIES model based on the fact that all the performance criteria inthis study outperformed the ARMA model to forecast water city demand in the Isfahan.Conclusion: Of a time series model structure to model ARMA in this research is to estimate the model andpredict the number the less time is required, and also can be used for modeling of other variables (such asincome and price) to this is helping to improve the models. Also, in ARMA TIME SERIES the best model for datawas selected according to the Schwarz Bayesian and Akaike criterion. Results indicate that the estimation ofwater demand using STRUCTURAL TIME SERIES method is more efficient than when ARMA TIME SERIES model isapplied. Therefore, STRUCTURAL TIME SERIES model can be used as an efficient tool for managers and planners in theManagement Departmentsin order to forecast water demand. Used was for compare the performance of thesetwo models of standard root mean square error (RMSE), mean absolute error (MAE) and mean absolutepercentage error (MAPE).

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APA: Copy

SADEGHI, H., AKHOOND ALI, A.M., HADDAD, M., & GOLABI, M.R.. (2015). MODELING AND FORECASTING OF WATER DEMAND IN ISFAHAN USING UNDERLYING TREND CONCEPT AND TIME SERIES. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), 29(2), 251-262. SID. https://sid.ir/paper/141067/en

Vancouver: Copy

SADEGHI H., AKHOOND ALI A.M., HADDAD M., GOLABI M.R.. MODELING AND FORECASTING OF WATER DEMAND IN ISFAHAN USING UNDERLYING TREND CONCEPT AND TIME SERIES. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY)[Internet]. 2015;29(2):251-262. Available from: https://sid.ir/paper/141067/en

IEEE: Copy

H. SADEGHI, A.M. AKHOOND ALI, M. HADDAD, and M.R. GOLABI, “MODELING AND FORECASTING OF WATER DEMAND IN ISFAHAN USING UNDERLYING TREND CONCEPT AND TIME SERIES,” JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), vol. 29, no. 2, pp. 251–262, 2015, [Online]. Available: https://sid.ir/paper/141067/en

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