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Quarterly water consumption data collected in a small water supply system were used for elaboration of a new water consumption modeling approach. In this paper, multi-distribution statistical analysis was performed. As the Anderson-Darling test proved, at least a half out of the ten tested theoretical probability distributions can be used for description of the water consumption. The application of the PWRMSE criterion made it possible to determine, which of the tested theoretical distributions is the best-fitted to the empirical data set. In the case of total daily water consumption for the group of the households, it was Johnson distribution, whereas for the average daily water consumption per capita, it was GEV distribution. Based on the best-fitted probability distribution, a 25-year water consumption simulation with the Monte Carlo method was conducted. Because methodology of this study is based on the probability distributions, even if the type of theoretical distribution of the water consumption will change, it will be still possible to use this simulation method by assuming the other distribution.
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180--197
Opis fizyczny
Bibliogr. 49 poz., rys., tab.
Twórcy
autor
- Department of Water Supply, Sewerage and Environmental Monitoring, Cracow University of Technology, Poland
autor
- Department of Sanitary Engineering and Water Management, University of Agriculture in Cracow, Poland
autor
- Department of Sanitary Engineering and Water Management, University of Agriculture in Cracow, Poland
Bibliografia
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- Froelich, W. (2015). Forecasting daily urban water demand using Dynamic Gaussian Bayesian Network. Proceedings of the 11th International Conference on Beyond Databases, Architectures and Structures, Ustroń, Poland, 26-29 May 2015, 333-342.
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- Młyński, D., Wałęga, A., Petroselli, A., Tauro, R., Cebulska, M. (2019). Estimating maximum daily precipitation in the upper Vistula basin, Poland. Atmosphere, 10(2), 1-17. DOI: 10.3390/atmos10020043
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- Parresol, B. (2003). Recovering parameters of Johnson’s SB Distribution. Research Paper SRS-31. Asheville, USA: Southern Research Station. Available online: https://www.fs.usda.gov/treesearch/pubs/5455.
- Pasela, R., Gorączko, M. (2013). Analysis of selected factors characterizing water consumption in multi-family buildings. Rocznik Ochrona Srodowiska, 15, 1658-1672 (in Polish).
- Pawełek, J. (2015). Water management in Poland in view of water supply and sewage disposal infrastructure development. Infrastructure and Ecology of Rural Areas,II/2, 367-376. DOI: 10.14597/INFRAECO.2015.2.2.029
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- Provost, S. B., Saboor, A., Cordeiro, G. M., Mansoor, M. (2018). On the q-generalized extreme value distribution. REVSTAT – Statistical Journal, 16(1), 45-70.
- Rathnayaka, K., Malano, H., Arora, M., George, B., Maheepala, S., Nawarathna, B. (2017). Prediction of urban residential end-use water demands by integrating known and unknown water demand drivers at multiple scales II: Model application and validation. Resources, Conservation and Recycling, 118, 1-12, DOI: 10.1016/j.resconrec.2016.11.015
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- Reynaud, A. (2015). Modelling household water demand in Europe - Insights from a cross-country econometric analysis of EU-28 countries. Technical Report for the Joint Research Centre of the European Commision, Publications Office of the European Union, Luxemburg. Available online: https://ec.europa.eu/jrc/en/publication/modelling-household-water-demand-europe-insights-cross-countryeconometric-analysis-eu-28-countries.
- Reynaud, A., Pons, M., Pesado, C. (2018). Household water demand in Andorra: Impact of individual metering and seasonality. Water, 10(3), 1-18. DOI: https://doi.org/10.3390/w10030321
- Ribeiro, A. S., Almeida, M. C., Palma, J. (2009). Uncertainty evaluation of multi-sensor flow measurement in a sewer system using Monte Carlo method. Proceedings of the XIX IMEKO World Congress: Fundamental and Applied Metrology, Lisbon, Portugal, 6-11 September 2009, 1287-1292.
- Rinaudo, J. D. (2015). Long-term water demand forecasting. Understanding and managing urban water in transition. Dordrecht, Netherlands: Springer, 239-268. DOI: 10.1007/978-94-017-9801-3_11
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- Sauri, D. (2019). The decline of water consumption in Spanish cities: structural and contingent factors. International Journal of Water Resources Development, 36(6), 909-925. DOI: https://doi.org/10.1080/07900627.2019.1634999
- Schleich, J., Hillenbrand, T. (2009). Determinants of residential water demand in Germany. Ecological Economics, 68(6), 1756-1769. DOI: 10.1016/j.ecolecon.2008.11.012
- Sikora, J., Woźniak, A., Zemanek, J. (2006). Water consumption by Bystra village dwellers in the light of survey research. Infrastructure and Ecology of Rural Areas, 3(2), 53-65 (in Polish).
- Taheriyoun, M., Moradinejad, S. (2015). Reliability analysis of a wastewater treatment plant using fault tree analysis and Monte Carlo simulation. Environmental Monitoring and Assessment, 187(1), 1-13. DOI: 10.1007/s10661-014-4186-7
- Tiwari, M. K., Adamowski, J. F. (2015). Medium-term urban water demand forecasting with limited data using an ensemble wavelet-bootstrap machine-learning approach. Journal of Water Resources Planning and Management, 141(2). DOI: 10.1061/(ASCE)WR.1943-5452.0000454
- Vijai, P., Sivakumar, B. (2018). Performance comparison of techniques for water demand forecasting. Procedia Computer Science, 143, 258-266. DOI: https://doi.org/10.1016/j.procs.2018.10.394
- Wałęga, A. (2016). The importance of calibration parameters on the accuracy of the floods description in the Snyder’s model. Journal of Water and Land Development, 28, 19-25. DOI: 10.1515/jwld-2016-0002
- Wałęga, A., Młyński, D., Bogdał, A., Kowalik, T. (2016). Analysis of the course and frequency of high water stages in selected catchments of the Upper Vistula Basin in the South of Poland. Water, 8(9), 1-15. DOI: 10.3390/w8090394
- Wu, G. Z., Sakaue, K., Murakawa, S. (2017). Verification of calculation method using Monte Carlo method for water supply demands of office building. Water, 9(6), 1-21. DOI:10.3390/w9060376
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a103c460-6b7f-4881-82f7-547fcef69fa0