Modeling of the loads of water supply networks and their subsequent forecasting is an element necessary for making optimum decisions in the process of planning the development and operation of the water supply networks. The results of this modeling are decisive for the selection of the diameters of the pipelines and their arrangement on the water demand area. This study presents the results of estimation of average values of loads for the selected investment variants. The aim of the article is to present the possibility of simulations and analyses of the geostatistical interpolation methods. Data input in the model regarded the fragment of the real water supply network administered by the Municipal Water and Sewerage Company in Warszawa. Results of the computer analyses for the presented investment variants were related to the operating data of the water supply network and the data on water demand for the years 2014-2017 and 2018-2025. The aim of this paper is to present the advantages of GIS for the water supply systems and to prove that using the appropriate IT system, with provision of proper data processing, may lead to decisions which are optimum in view of the established, often very complex criteria.
If the inline PDF is not rendering correctly, you can download the PDF file here.
[l] United Nations Population Fund. State of the World Population 2007: Unleashing the Potential of Urbangrowth. New York: United Nations Population Fund; 2007.
 House-Peters LA, Chang H. Urban water demand modeling: Review of concepts, methods, and organizing principles. Water Resour Res. 2011;47:1-15. DOI: 10.1029/2010WR009624.
 Anisha G, Kumar A, Ashok Kumar J, Suvarna Raju P. Analysis and design of water distribution network using EPANET for Chirala Municipality in Prakasam District of Andhra Pradesh. Int J Eng Appl Sci. 2016;3(4):53-60. https://www.ijeas.org/download_data/IJEAS0304026.pdf.
 Boulos FP, Jacobsen BL, Heath EJ, Kamojjala S. Real-time modeling of water distribution systems: A case study. J Am Water Works Assoc. 2014;106(9):391-401.DOI: 10.5942/jawwa.2014.106.0076.
 BañosR, Gil C, Reca J, Montoya GF. A memetic algorithm applied to the design of water distribution networks. Appl Soft Comput. 2010;10(1):261-266. DOI: 10.1016/j.asoc.2009.07.010.
 Lee SJ, Wentz EA. Applying Bayesian Maximum Entropy to extrapolating local-scale water consumption in Maricopa County, Arizona. Water Resour Res. 2008;44, W01401. DOI: 10.1029/2007WR006101.
 Sunela MI, Puust R. Real time water supply system hydraulic and quality modeling - a case study. Procedia Eng. 2015;119:744-752. DOI: 10.1016/j.proeng.2015.08.928.
 Shandas V, Parandvash GH. Integrating urban form and demographics in water-demand management: An empirical case study of Portland, Oregon. Environ Planning B Plannning Des. 2010;37:112-128. DOI: 10.1068/b35036.
 Franczyk J, Chang H. Spatial analysis of water use in Oregon, USA, 1985-2005. Water Resour Manage. 2009;23:755-774. DOI: 10.1007/s11269-008-9298-9.
 Savelieva E. Using ordinary kriging to model radioactive contamination data. Appl GIS. 2005;1(2):10-01-10-10. DOI: 10.2104/ag050010.
 Bancheri M, Serafin F, Bottazzi M, Abera W, Formetta G, Rigon R. The design, deployment, and testing of kriging models in GEOframe with SIK-0.9.8. Geosci Model Dev. 2018;11:2189-2207. DOI: 10.5194/gmd-11-2189-2018.
 Qiao P, Lei M, Yang S, Yang J, Guo G, Zhou X. Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing. Environ Sci Pollut Res Int. 2018;25(16):15597-15608. DOI: 10.1007/s11356-018-1552-y.
 Goovaerts P. Geostatistics for Natural Resources Evaluation. New York: Oxford University Press; 1997. ISBN: 0195115384.
 Isaaks EH, Srivastava RM. An Introduction to Applied Geostatistics. New York: Oxford University Press;1989. ISBN: 9780195050134.
 Farmer WH. Ordinary kriging as a tool to estimate historical daily streamflow records. Hydrol Earth Syst Sci. 2016;20:2721-2735. DOI: 10.5194/hess-20-2721-2016.
 Zhang J, Li X, Yang R, Liu Q, Zhao L, Dou B. An extended kriging method to interpolate near-surface soil moisture data measured by wireless sensor networks. Sensors 2017;17(6):1390. DOI: 10.3390/s17061390.
 Szeląg B, Gawdzik A, Gawdzik A. Application of selected methods of black box for modelling the settleability process in wastewater treatment plant. Ecol Chem Eng S. 2017;24(1):119-127. DOI: 10.1515/eces-2017-0009.
 Miller T, Poleszczuk G. Prediction of the seasonal changes of the chloride concentrations in urban water reservoir. Ecol Chem Eng S. 2017;24(4):595-611. DOI: 10.1515/eces-2017-0039.
 Chai T, Draxler R. Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature. Geosci Model Dev. 2014;7:1247-1250. DOI: 10.5194/gmd-7-1247-2014.
 Zeng W, Lei G, Zhang H, Hong M, Xu C, Wu J, et al. Estimating root zone moisture from surface soil using limited data. Ecol Chem Eng S. 2017;24(4):501-516. DOI: 10.1515/eces-2017-0033.
 Hengl T, Nussbaum M, Wright MN, Heuvelink GBM, Gräler B. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. Peer J. 2018;6:e5518. DOI: 10.7717/peerj.5518.
 Wackernagel H. Multivariate Geostatistics. An Introduction with Applications. Third, completely revised edition. Berlin: Springer-Verlag; 2003. DOI: 10.1007/978-3-662-05294-5.
 Zarychta R, Zarychta A. Application of ordinary kriging to reconstruct and visualise the relief in the location of an open pit sand mine. Cartography and Remote Sensing, Special issue: Measurement Technologies in Surveying. 2013;133-146. ISBN: 9788361576267.
 Klauberg C, Hudak AT, Bright BC, Boschetti L, Dickinson MB, Kremens RL, et al. Use of ordinary kriging and Gaussian conditional simulation to interpolate airborne fire radiative energy density estimates. Int J Wildland Fire. 2018;27(4):228-240. DOI: 10.1071/WF17113.