Modeling the urban water demand in Mashhad based on fuzzy regression model

Document Type : Original Article

Authors

1 Professor, Water Engineering Department, Ferdowsi University of Mashhad

2 Associate professor, Department of Water Engineering , Ferdowsi University of Mashhad., Mashhad., Iran

3 Water Engineering Department, Ferdowsi University of Mashhad, Iran

Abstract

Supplying the tap water in Mashhad city is going to face serious challenges and threats in the future. The population of this city is more than 3 million and its pilgrim is estimated at 25 million per year. Urbanization in Mashhad city and around it is growing. The range of this study is including Mashhad city along with Golbahar, Chenaran, and Ghuchan. The excessive development of these cities leads Mashhad city to physical water scarcity in long term. For this purpose, the effective factors on water demand in Mashhad city have been identified. For this purpose the most desirable urban water demand function has been extracted, by using the Stone Gary method. It is worth mentioning that in order to investigate economic and social uncertainties in the model, the results of previous researches and the opinion of experts have been used; then, the effective uncertainties on the elements are determined by using the fuzzy linear regression model. Finally, urban water demand function of the Mashhad city for 2010-2017 is calibrated and used to estimate water demand from 2018 to 2070. The results show that the demand function of Mashhad city is strongly influenced by the time between receipt of the bill and the household composition. So these two factors can be considered for demand management. At the end, the model accuracy is based on the R^2 that is equivalent to 0.921 which indicates the proper performance of this model in predicting urban water demand

Keywords


آمار و اطلاعات شرکت آب و فاضلاب شهر مشهد، 1394.
داوری، کامران، عمرانیان خراسانی، حمید، قنبری، فریبا، 1393، تدبیر آب مشهد، به سفارش شرکت آب و فاضلاب شهر مشهد، 1393.
پژویان، جمشید، حسینی، سید شمس­الدین (1382). برآورد تابع تقاضای آب خانگی (مطالعه موردی .47-67 :( شهر تهران). فصلنامه پژوهشهای اقتصادی ایران ( 16)
تابش، مسعود، دینی، مهدی، خوش خلق، علی­جعفر، زهرایی، بنفشه (1388). برآورد مصرف روزانه آب تهران، تحقیقات منابع آب ایران 4:(2). 65-57
خوش اخلاق، رحمان، شهرکی، جواد(1386). برآورد تابع تقاضای آب خانگی در شهر زاهدان.
سجادیفر، سیدحسین، خیابانی، ناصر (1389). مدل سازی تقاضای آب خانگی شهر با استفاده از روش مدل عوامل تصادفی، مطالعه موردی: شهر اراک. فصلنامه آب و فاضلاب، 22:(22) .59- 68
صبوحی، محمود، نوبخت، مسعود (1387). برآورد تابع تقاضای آب شهر پردیس، دانشکده کشاورزی، دانشگاه زابل، مجله آب و فاضلاب: 69- 74
قندهاری، احمد، داوری، کامران، قهرمان، بیژن، 1395، اعتمادپذیری سیاست­های پروژه محور؛ (مطالعه موردی: ارزیابی ریسک برنامه های تامین آب مشهد)، فصلنامه مطالعات راهبردی سیاست گذاری عمومی، دوره ششم / شماره 21 / زمستان 1395
Al-Kandari, A.M., Soliman, S.A., El-Hawary, M.E. (2004). Fuzzy short-term electric load forecasting, Electrical Power and Energy Systems, 26, 111-122.
Dagnew, D. (2012). Factors determining residential water demand in north Western Ethiopia, The case of Merawi, A project paper presented to the faculty of the graduate school of Cornell University in partial fulfillment of the requirements for the Degree of Master of Professional Studies.
Dharmaratna, D., & Harris, E. (2010). Estimating residential water demand using  the Stone-Geary functional form: The case of Sri Lanka, paper provided by Monash University, Department of Economics in its series Monash Economics, Working Paper number: 46-10.
Kumar A., Kaur J., Singh P.; A new method for solving fully fuzzy linear programming problems; Applied Mathematical Modelling, Vol. 35, pp. 817-823, 2011.
Parker, J. M., & Wilby, R. L. (2013). Quantifying household water demand: A review of theory and practice in the UK, Water Resour Manage, 27(4): 981–1011.
Saunders,R.J.(1960).Forecasting water demand :An inter and intercommunity Study .West Virginia University .Business and Economic Studies.11(2)
Taghizadeh, MR., Shakouri G, H., Menhaj, MB. & Kazemi, A. (2008). Design of a multi-level fuzzy linear regression model for forecasting transport energy demand, The 13th IIES International Oil & Gas Conference.
Tanaka H, Uejima S, Asai K (1982a). Fuzzy linear regression model. IEEE Tans System Man Cybernetics 12: 903- 907.
Tanaka H, Uejima S, Asai K (1982b). Fuzzy linear regression model. IEEE T. Syst. Man Cyb. 10: 2933–2938