Selection of the Best Statistical Index of Nodal Pressure Values for Use in Calibrating the Hydraulic Model of the Water Distribution Network Based on Field Data Processing

Document Type : Case study


1 Assoc. Prof., Dept. of Civil Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran

2 MSc. Student, Dept. of Civil Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran

3 Assoc. Prof., Faculty of Civil Engineering, Urmia University of Technology, Urmia, Iran

4 Hydraulic Modelling Engineer, Jacobs Engineering Group, Toronto, Ontario, Canada


Due to the widespread use of computers and measuring equipment in the operation of water distribution networks, a large amount of data is recorded for monitoring and evaluating the performance of water distribution networks and it is used in the modeling and calibration process. The management of these data is very necessary to achieve more accurate models on the one hand and the speed of their processing on the other hand. In this research, the purpose is to process nodal pressure field data to select the best statistical indicators for calibrating the water distribution network model. For this purpose, more than 5500 data collected in 22 stations of Ahar water distribution network and 12 stations of Oshnaviyeh water distribution network have been analyzed. First, by categorizing the data with Sturges experimental method, the probability of the data being placed in the central index categories of average, median, and mode and other categories in different stations in the times of minimum, maximum, and average consumption has been determined, and by summarizing the results, the best central index has been selected. Then, to analyze how the data changes, the minimum and maximum values, the range of variation, and the standard deviation of the data are presented along with the histogram of the categories. The trend of data variations in different stations in the minimum, maximum, and average consumption times shows that there is no specific harmony for data variations, so the maximum or minimum values of the range of variation and the standard deviation of the data are moved spatially in the stations. Also, the process of data allocation to categories shows that in the Ahar water distribution network, most data is allocated to the mode category at about 28.6 percent, followed by other categories at about 26.3 percent. Also, in the Oshnaviyeh water distribution network, the highest allocation is related to other categories with about 30.2 percent, followed by the mode category with 27.2 percent. Considering the multiplicity and dispersion of other categories and the unity of the mode category, the mode category is the best choice for both case studies. In general, by using mode values instead of other central indicators in the calibration of water distribution networks, due to the effectiveness of more field data, more favorable results will be obtained in the construction of the network model.


Amini, G., Entezam, H., Sadeghpour, A. & Davood Abedi, A. 2018a. Application of data mining to identify subscribers with unauthorized use of water (case study of Qom water and Wastewater Company). 2nd Iran Water and Wastewater Science Engineering Congress and National Conference on Demand and Supply of Drinking Water and Sanitation, Isfahan, Iran. (In Persian)
Amini, G., Entezam, H., Sadeghpour, A. & Davood Abedi, A. 2018b. Identification and extraction of water consumption patterns by data mining (case study of Qom water and wastewater company). 2nd Iran Water and Wastewater Science Engineering Congress and National Conference on Demand and Supply of Drinking Water and Sanitation, Isfahan, Iran. (In Persian)
Amini, G. 2020. Modeling of unauthorized water consumption detection (case study: Qom). Journal of Water and Wastewater, 31(4), 184-193. (In Persian).
Avand, M. T., Janizadeh, S. & Farzin, M. 2019. Groundwater potential determination on Yasouj-Sisakht area using random forest and generalized linear statistical models. Range and Watershed Management 72, 609-623. (In Persian)
Bohrani, N. 2011. Probability and Statistics for Engineers. Gothenburg Pub., Tehran, Iran. (In Persian)
Dini, M. & Tabesh, M. 2014. A new method for simultaneous calibration of demand pattern and Hazen-Williams coefficients in water distribution systems. Water Resources Management, 28, 2021-2034.
Dini, M. & Tabesh, M. 2017. Water distribution network quality model calibration: a case study–Ahar. Water Science and Technology: Water Supply, 17, 759-770.
Dini, M. & Tabesh, M. 2019. Optimal renovation planning of water distribution networks considering hydraulic and quality reliability indices. Urban Water Journal, 16, 249-258.
Ghorbani, K. 2016. Evaluation of hydrological and data mining models in monthly river discharge simulation and prediction (case study: Araz-Kouseh watershed). Journal of Water and Soil Conservation, 23, 203-217. (In Persian)
Hashemi, S., Filion, Y. & Speight, V. 2018. Identification of factors that influence energy performance in water distribution system mains. Water, 10, 428.
Nazmfar, H., Eshgheichharborj, A., Alvai, S. & Eshghei, S. 2018. Spatial analysis of the healthy city indicators in urban settlements (case study: Ardabil province). Journal of Environmental Science and Technology, 20, 265-282. (In Persian).
Sattari, M. T., Mirabbasi Najaf Abadi, R. & Abbasgoli Naebzad, M. 2017. Surface water quality prediction using data mining method (case study: rivers of northern side of Sahand Mountain). Iranian Journal of Ecohydrology, 4, 407-419. (In Persian)
Shirzad, A., Heidarzadeh, M. & Mohamadi, M. A. 2020. Providing hydraulic model and evaluating reliability of water distribution networks (case study: Oshnaviyeh City). Journal of Water and Wastewater Science and Engineering, 5, 39-47. (In Persian).
Taj Abadi, Y., Jalili Ghazizade, M. & Moslehi, I. 2018. A field data-based method to determine the pressure-burst relationships in urban water distribution networks. Environmental Sciences, 16, 127-140. (In Persian)
Talebian, M., Ahmadifar, H., Mirroshan, S. & Shakeri, M. 2019. Forecasting water consumption using data mining methods. 3rd International Conference on Smart Computing. Rasht, Iran. (In Persian)