Identification of Meteorological Parameters Affecting Water Consumption in Household Sector of Qom

Document Type : Research Paper

Authors

1 MSc. of Statistics, Water and Wastewater Co., Qom Province, Qom, Iran

2 MSc. of Economical & Social Statistis, Department of Math and Computer, Shahid Chamran University Ahvaz, Iran

Abstract

Prediction of water consumption and its effective factors is an important step in water crisis management. Studies showed that meteorological parameters are considered as the most important factor for short-term prediction of water consumption. In this research, cluster-based sliced inverse regression method was used to identify the meteorological variables affecting the household water consumption in Qom. In addition to dimension reduction, this method can be used to remove collinearity. The data consisted of seven meteorological parameters and monthly household water consumption from 2001 to 2013. Data analysis indicated that instead of seven primary variables, only two new components which are linear combinations of independent variables can be used. The negatively charged maximum wind speed and relative humidity (0.757 and 0.4) of the first component, and the negatively charged average minimum temperature (0.753) and positively charged average air temperature (0.634) of the second component had the greatest impact on the components. The regression analysis indicated that the average minimum temperature coefficient 0.018, the maximum wind speed coefficient -0.004, and determination coefficient 0.92% are significant. Comparing the method proposed in this paper with the usual method of principal component analysis (PCA) for multivariate data analysis indicated that cluster-based sliced inverse regression has fewer errors. Moreover, noticing the impact of collinearity on the outputs of neural networks, the method proposed in this paper had better performance than the usual methods and consequently predicts water consumption.

Keywords

Main Subjects

References

 Adamowski, J., Fung, C.H., Prasher, S.O., Zielinski, B. & Sliusarieva, A., 2012, "Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal", Journal of Water Resources Research, 48, W01528.
Amini, Gh., 2015, "Estimating household water demand of Qom by artificial neural networks and log linear regression", 1st Water Sciences and Engineering Conference, Tehran, Iran. (In Persian)
Bakker, M., Duist, H., Schagen, K., Vreeburg, J. & Rietveld, L., 2014, " Improving the performance of water demand forecasting models by using weather input", 12th International Conference on Computing and Control for the Water Industry, CCWI2013, Procedia Engineering, 70, 93-102.
Becker, C. & Gather, U., 2007, "A note on the choice of the number of slices in sliced inverse regression", Working paper, Dept. of Statistic, University of Dortmund, Germany.
Cook, R.D., 1998, Regression graphics: Ideas for studying regressions through graphics. Wiley, New York.
Eaton, M.L., 1986, "A characterization of spherical distributions", Journal of Multivariate Analysis, 20, 272-276.
Fisher, R.J., 1922, "On the mathematical foundations of theoretical statistics", Philosophical Transactions of the Royal Statistical Society, 222, 309-368.
Joo, C.N., Koo, J.K. & Yu, M.J., 2002, "Application of short-term water demand prediction model to Seoul", Journal of Water Sciences and Technology, 46(6-7), 255-261.
Kuentz, V. & Saracco, J., 2010, "Cluster-based sliced inverse regression", Journal of Statistical Society, 39, 251-267.
Li, K.C., 1991, "Sliced inverse regression for dimension reduction (with discussion)", Journal of Statistical Association, 86, 316-327.
Li, L., Cook, R.D. & Nachtsheim, C.J., 2004, "Cluster-based estimation for sufficient dimension reduction", Computational Statistics & Data Analysis, 47, 175-193.
Lott, C., Tchigriaeva, E. & Rollins, K., 2013, The effects of climate change on residential municipal water demand in Nevada, American Geophysical Union.
Menhaj, M.B., 2005, Basics of neural network, Prof Hesabi Inc., Tehran. (In Persian)
Miaou, S.P., 1990, "A class of time-series urban water demand models nonlinear climatic effects", Water Resources Research, 6(2), 169-178.
Naik, P., Hagerty, M.R. & Tsai, C.L., 2000, " A new dimension reduction approach for data-rich marketing environments: Cliced inverse regression ", Journal of Marketing Research, 37, 88-101.
Praskievicz, S. & Chang, H., 2009, "Identifying the relationships between urban water consumption and weather variables in Seoul, Korea", Journal of Physical Geography, 30(4), 324-337.
PBO.,1998, Qom population projection in the horizon 2021, Qom, Iran. (In Persian)
Sajadifar, S.H. & Khiabani, N., 2011, "Modeling of residential water demand using random effect model, case study: Arak city", Journal of Water and Wastewater, 22 (3), 59-68. (In Persian)
Stark, H.L., Stanley, J.S. & Buchanan, I.D., 2000, "The application of artificial neural networks to water demand modeling ", CSCE 28th Annual Conference, London.
Tabesh, M. & Dini, M., 2005, "Short-term urban water consumption estimation with fuzzy logic based on temperature and humidity", 5th Iranian Hydraulic Conference, Kerman. (In Persian)
Tabesh, M. & Dini, M., 2010, "Forecast daily water demand using artificial neural networks, case study: Tehran city", Journal of Water and Wastewater, 21(1), 84-95. (In Persian)
Tabesh, M., Dini, M., Khoshkholgh, A. J. & Zahraie, B., 2007, "Estimation of tehran daily water demand using time series analysis", Journal of Iran-Water Resources Research, 4 (2), 57-65. (In Persian)
Tabesh, M., Dini, M. & Naseri, M., 2006, "Short-term urban water consumption estimation using neuro-fuzzy sugeno model (two-parameter models)", 7th International Congress of Civil Engineering, Tehran. (In Persian)
Tabesh, M., Gooshe, S. & Yazdanpanah, M.J., 2007, "Short-term water demand forecasts in Tehran, using neural networks", Tehran University, College of Engineering, 41(1), 11-24. (In Persian)
Weisberg, S., 2009, "The dr package", http://www.r-project.org>(May 2016).
Willsie, R.H. & Pratt, H.L., 1974, "Water use relationships and projection corresponding with regional growth", Water Resources Bulletin, 10(2), 360-371.
Wong, S.T., 1972, "A model on municipal water demand: A case study of northeastern Illinois", Land Econ, 48(1), 34-44.
Young, R.A., 1973 "Price elasticity of demand for municipal water: A case study of Tucson and Arizona", Journal of Water Resource Research, 9(4), 1068-1072.
Journal of Water and Wastewater; Ab va Fazilab ( in persian )
Volume 29, Issue 2 - Serial Number 114
May and June 2018
Pages 48-58

Files

Share

How to cite

Statistics