Statistical analysis of the urban water consumption for administrative-business sector

Gul’naz I. Galimova; Dinar T. Yakupov

Gul’naz I. Galimova Kazan Federal University, Naberezhnye Chelny Institute
Dinar T. Yakupov Kazan National Research Technical University named after A.N. Tupolev – KAI

Keywords: Daily water consumption, time dynamic row, regression analysis, autocorrelation, forecasting.

Abstract

In this work statistical forecasting of daily water consumption of the administrative and business sector of the city is considered for what the technique including 5 stages is offered. According to this technique, at the first stage the choice of set of the statistics influencing water consumption volume is derivative, the basic statistical data (BSD) are collected. Further, the main statistical characteristics of ISD are calculated: estimates of population mean, average quadratic deviations, medians, asymmetries, excesses, errors of calculation of averages, asymmetries, excesses. At the following stage the regression equation is received, assessment of extent of influence of factors on water consumption on their specific weight and coefficients of elasticity is made, forecasting on the basis of the received regression equation is executed, assessment of the forecast is made by criteria of R2, MAE, MAPE, MSE, S/

Downloads

Download data is not yet available.

Author Biographies

Gul’naz I. Galimova, Kazan Federal University, Naberezhnye Chelny Institute

Kazan Federal University, Naberezhnye Chelny Institute

Dinar T. Yakupov, Kazan National Research Technical University named after A.N. Tupolev – KAI

Kazan National Research Technical University named after A.N. Tupolev – KAI

References

Adamowski, J. F. (2008). Peak daily water demand forecast modeling using artificial neural networks. Journal of Water Resources Planning and Management, 134(2), 119-128.

Anderson, R. L., Miller, T. A., & Washburn, M. C. (1980). Water savings from lawn watering restrictions during a drought year, Fort Collins, Colorado 1. JAWRA Journal of the American Water Resources Association, 16(4), 642-645.

Bougadis, J., Adamowski, K., & Diduch, R. (2005). Short‐term municipal water demand forecasting. Hydrological Processes: An International Journal, 19(1), 137-148.

Howe, C. W., & Linaweaver, F. P. (1967). The impact of price on residential water demand and its relation to system design and price structure. Water Resources Research, 3(1), 13-32.

Hughes, T. (1980). Peak period design standards for small western US water supply systems 1. Journal of the American Water Resources Association, 16(4), 661-667.

Maidment, D. R., Miaou, S. P., & Crawford, M. M. (1985). Transfer function models of daily urban water use. Water Resources Research, 21(4), 425-432.

Maidment, D., & Parzen, E. (1984). Monthly water use and its relationship to climatic variables in Texas. Water Resour. Bull, 19(8), 409-418.

Miaou, S. P. (1990). A class of time series urban water demand models with nonlinear climatic effects. Water Resources Research, 26(2), 169-178.

Oh, H. S., & Yamauchi, H. (1974). An economic analysis of the patterns and trends in water consumption within the service area of the Honolulu Board of Water Supply. Univ. of Honolulu Rep, 84.

Zhou, S. L., McMahon, T. A., Walton, A., & Lewis, J. (2000). Forecasting daily urban water demand: a case study of Melbourne. Journal of hydrology, 236(3-4), 153-164.