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Forecasting of Istanbul's Clean Water Consumption With Fuzzy Time Series Approaches

Year 2012, Volume: 9 Issue: 2, 1 - 11, 15.08.2012

Abstract

Accurate forecasting of water consumption is very important for planning and managing water sources during now that the global warming and climale change have distinctly appeared. While the classic methods have been frequently used for the forecasting of water consumption in the literature, fuzzy logic and artificial neural networks have also been among the efficiently used methods over the last years. Since conventional estimation methods require several assumptions, methods such as fuzzy logic and artificial networks are utilized to obtain more efficient and better results in the recent years. In this study, forecasting the quantity of Istanbul water consumption with various fuzzy time series approaches is aimed, and the results are evaluated.

References

  • Aladag, C. H. , Basaran, M. A., Egrioglu, E., Yolcu, U., Uslu, V. R., 2009. Forecasting in High Order Fuzzy Times Series by Using Neural Networks to Define Fuzzy Relations, Expert Systems with Applications, 36, 4228-4231.
  • Alpaslan, F., Cagcag, O., Aladag, C. H., Yolcu, n, Egrioglu, E., 2011. A Novel Seasonal Fuzzy Time Series Method, FUZZYSS'11: The Second International Fuzzy Systems Symposium, Proceeding Book, Editors: C. Gokceoglu, H. C. Aladag, A. Akgun, Page: 50-55.
  • Altunkaynak, A., Ozger, M., Cakmakcı, M., 2005. Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach, Water Resources Management, 19, 641-654.
  • Bougadis, J., Adamowski, K., Diduch, R, 2005. Short-term Municipal Water Demand Forecasting, Hydrological Processes, 19, 137 -148.
  • Caiado, J, 2007. Forecasting Water Consumption in Spain Using Univariate Time Series Models, Munich Personal RePEc Archive (Online at, http://mpra.ub.unimuenchen.de/6610/).
  • Caiado, J., 2009. Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Consumption, Munich Personal RePEc Archive, (Online at http://mpra.ub.uni-muenchen.de/l5242/).
  • Calvo, I.P., Gutierez-Estrada, J. C., 2009. Improved Irrigation Water Demand Forecasting Using a Soft-Computing Hybrid Model, Biosystems Engineering, 102, 202-218.
  • Calvo, I. P., Roldan, J., Lopez-Luque, R, Gutierez-Estrada, J. C., 2003. Demand Forecasting for Irrigation Water Distribution Systems, Journal of Irrigation and Drainage Engineering, 129(6), 422-431.
  • Chen, S. M., 1996. Forecasting Enrollments Based on Fuzzy Time-Series, Fuzzy Sets and Systems, 81, 311-319.
  • Chen, S. M., 2002. Forecasting Enrollments Based on High Order Fuzzy Time Series, Cybernetics and Systems, 33:1-16.
  • Cutore, P., Campisano, A., Kapelan, A., Modica, c., Savic, D., 2008. Probabilistic prediction of urban water consumption using the SCEM-UA algorithm, Urban Water Journal, 5(2),125-132.
  • Egrioglu, E., Aladag, C. H., Yolcu, U., Uslu, V. R., Basaran, M. A., 2010. Finding an Optimal Interval Length in High Order Fuzzy Time Series, Expert Systems with Applications, 37,5052-5055.
  • Egrioglu, E., Aladag, C. H., Yolcu, U., Basaran, M. A and Uslu, V. R, 2009. A New Hybrid Approach Based on SARIMA and Partial High Order Bivariate Fuzzy Time Series Forecasting Model. Expert Systems with Applications, 36, 7424-7434.
  • Firat, M., Yurdusev, M. A., Turan, M. E., 2009. Evaluation of Artificial Neural Network Techniques for Municipal Water Consumption Modeling, Water Resour Manage, 23, 617-632.
  • Froukh, M. L., 2001. Decision-Support System for Domestic Water Demand Forecasting and Management, Water Resources Management, 15, 363-382.
  • Huarng, K., 2001. Effective Length of Intervals to Improve Forecasting in Fuzzy Time Series, Fuzzy Sets and Systems, 123, 387-394.
  • Lian, T. H., Liu, Q. J, Wang, J.C., 2008. Water Demand Forecast Based on ARIMA Time-series Identification, Control Engineering of China, 81.
  • Mohamed, M. M., Al-Mualla, A. A., 2010. Water Demand Forecasting in Umm AlQuwain (UAE) Using the IWR-MAIN Specify Forecasting Model, Water Resour Manage, 44, 4093-4120.
  • Nieswiadomy, M. L., Molina, D. J., 1989. Compating Residential Water Demand Estimates Under Decreasing and Increasing Block Rates Using Household Data, Land Economics, 65(3), 280-289.
  • Roberto, M. E., Celine, N., 2004. Is All Domestic Water Consumption Sensitive to Price Control, Applied, 36, 1697-1 703.
  • Shang, F., Vber, J. G., Waanders, B. G. B., Boccelli, D., Janke, R., 2006. Real Time Water Demand Estimation in Water Distribution System, 8th Annual Water Distribution Systems Analysis Symposium, Cincinnati, Ohio, USA, August 27-30.
  • Song, Q., Chissom, B. S., 1993. Fuzzy Time Series and Its Models, Fuzzy Sets and Systems, 54, 269-277.
  • Thomas, M., Fullerton, J, Arturo, E., 2004. Short-term Water Consumption Dynamics in El Paso, Texas, Water Resources Research, 40, doi: 10.1029/2004WR00326.
  • Thomas, M. F. J., Roberto, T., Jorge, E. M. C., 2007. An Empirical Analysis of Tijuana Water Consumption, Atl. Econ J, 35, 357-369.
  • Thomas, M. F. J, Roberto, T., Martha, P. B., 2006. Short-Term Water Consumption Patterns in Ciudad Jua'rez, Mexico, Atlantic Economic Journal, 34, 467-479.
  • Uslu, V. R., Aladag, C. H., Yolcu, U., Egrioglu, E., 2010. A new hybrid approach for forecasting a seasonal fuzzy time series. 1st International Symposium On Computing In Science & Engineering, Izmir, Turkey.

İstanbul Temiz Su Tüketiminin Bulanık Zaman Serisi Yaklaşımları ile Öngörüsü

Year 2012, Volume: 9 Issue: 2, 1 - 11, 15.08.2012

Abstract

Su tüketiminin doğru öngörülmesi, iklim değişikliklerinin son derece yoğun hissedildiği günümüzde, kısıtlı su kaynaklarının planlanması ve yönetimi açısından büyük önem arz etmektedir. Literatürde, su tüketim öngörüsünde, klasik yöntemler kullanılırken, bulanık mantık ve yapay sinir ağları da son zamanlarda etkin bir şekilde kullanılan yöntemler arasındadır. Tahminde kullanılan klasik yöntemlerin birçok varsayım içermesi nedeniyle, son yıllarda etkin ve daha iyi sonuçlar elde etmek için bulanık mantık ve yapay sinir ağları gibi yöntemlerden yararlanılmaktadır. Bu çalışmada İstanbul su tüketiminin miktarı çeşitli bulanık zaman serisi yöntemleri ile öngörülmesi amaçlanarak elde edilen sonuçlar değerlendirilmiştir.

References

  • Aladag, C. H. , Basaran, M. A., Egrioglu, E., Yolcu, U., Uslu, V. R., 2009. Forecasting in High Order Fuzzy Times Series by Using Neural Networks to Define Fuzzy Relations, Expert Systems with Applications, 36, 4228-4231.
  • Alpaslan, F., Cagcag, O., Aladag, C. H., Yolcu, n, Egrioglu, E., 2011. A Novel Seasonal Fuzzy Time Series Method, FUZZYSS'11: The Second International Fuzzy Systems Symposium, Proceeding Book, Editors: C. Gokceoglu, H. C. Aladag, A. Akgun, Page: 50-55.
  • Altunkaynak, A., Ozger, M., Cakmakcı, M., 2005. Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach, Water Resources Management, 19, 641-654.
  • Bougadis, J., Adamowski, K., Diduch, R, 2005. Short-term Municipal Water Demand Forecasting, Hydrological Processes, 19, 137 -148.
  • Caiado, J, 2007. Forecasting Water Consumption in Spain Using Univariate Time Series Models, Munich Personal RePEc Archive (Online at, http://mpra.ub.unimuenchen.de/6610/).
  • Caiado, J., 2009. Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Consumption, Munich Personal RePEc Archive, (Online at http://mpra.ub.uni-muenchen.de/l5242/).
  • Calvo, I.P., Gutierez-Estrada, J. C., 2009. Improved Irrigation Water Demand Forecasting Using a Soft-Computing Hybrid Model, Biosystems Engineering, 102, 202-218.
  • Calvo, I. P., Roldan, J., Lopez-Luque, R, Gutierez-Estrada, J. C., 2003. Demand Forecasting for Irrigation Water Distribution Systems, Journal of Irrigation and Drainage Engineering, 129(6), 422-431.
  • Chen, S. M., 1996. Forecasting Enrollments Based on Fuzzy Time-Series, Fuzzy Sets and Systems, 81, 311-319.
  • Chen, S. M., 2002. Forecasting Enrollments Based on High Order Fuzzy Time Series, Cybernetics and Systems, 33:1-16.
  • Cutore, P., Campisano, A., Kapelan, A., Modica, c., Savic, D., 2008. Probabilistic prediction of urban water consumption using the SCEM-UA algorithm, Urban Water Journal, 5(2),125-132.
  • Egrioglu, E., Aladag, C. H., Yolcu, U., Uslu, V. R., Basaran, M. A., 2010. Finding an Optimal Interval Length in High Order Fuzzy Time Series, Expert Systems with Applications, 37,5052-5055.
  • Egrioglu, E., Aladag, C. H., Yolcu, U., Basaran, M. A and Uslu, V. R, 2009. A New Hybrid Approach Based on SARIMA and Partial High Order Bivariate Fuzzy Time Series Forecasting Model. Expert Systems with Applications, 36, 7424-7434.
  • Firat, M., Yurdusev, M. A., Turan, M. E., 2009. Evaluation of Artificial Neural Network Techniques for Municipal Water Consumption Modeling, Water Resour Manage, 23, 617-632.
  • Froukh, M. L., 2001. Decision-Support System for Domestic Water Demand Forecasting and Management, Water Resources Management, 15, 363-382.
  • Huarng, K., 2001. Effective Length of Intervals to Improve Forecasting in Fuzzy Time Series, Fuzzy Sets and Systems, 123, 387-394.
  • Lian, T. H., Liu, Q. J, Wang, J.C., 2008. Water Demand Forecast Based on ARIMA Time-series Identification, Control Engineering of China, 81.
  • Mohamed, M. M., Al-Mualla, A. A., 2010. Water Demand Forecasting in Umm AlQuwain (UAE) Using the IWR-MAIN Specify Forecasting Model, Water Resour Manage, 44, 4093-4120.
  • Nieswiadomy, M. L., Molina, D. J., 1989. Compating Residential Water Demand Estimates Under Decreasing and Increasing Block Rates Using Household Data, Land Economics, 65(3), 280-289.
  • Roberto, M. E., Celine, N., 2004. Is All Domestic Water Consumption Sensitive to Price Control, Applied, 36, 1697-1 703.
  • Shang, F., Vber, J. G., Waanders, B. G. B., Boccelli, D., Janke, R., 2006. Real Time Water Demand Estimation in Water Distribution System, 8th Annual Water Distribution Systems Analysis Symposium, Cincinnati, Ohio, USA, August 27-30.
  • Song, Q., Chissom, B. S., 1993. Fuzzy Time Series and Its Models, Fuzzy Sets and Systems, 54, 269-277.
  • Thomas, M., Fullerton, J, Arturo, E., 2004. Short-term Water Consumption Dynamics in El Paso, Texas, Water Resources Research, 40, doi: 10.1029/2004WR00326.
  • Thomas, M. F. J., Roberto, T., Jorge, E. M. C., 2007. An Empirical Analysis of Tijuana Water Consumption, Atl. Econ J, 35, 357-369.
  • Thomas, M. F. J, Roberto, T., Martha, P. B., 2006. Short-Term Water Consumption Patterns in Ciudad Jua'rez, Mexico, Atlantic Economic Journal, 34, 467-479.
  • Uslu, V. R., Aladag, C. H., Yolcu, U., Egrioglu, E., 2010. A new hybrid approach for forecasting a seasonal fuzzy time series. 1st International Symposium On Computing In Science & Engineering, Izmir, Turkey.
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Applied Statistics
Journal Section Research Articles
Authors

Faruk Alpaslan

Özge Cağcağ This is me

Damla İlter Fakhourı

Ufuk Yolcu

Publication Date August 15, 2012
Published in Issue Year 2012 Volume: 9 Issue: 2

Cite

APA Alpaslan, F., Cağcağ, Ö., İlter Fakhourı, D., Yolcu, U. (2012). İstanbul Temiz Su Tüketiminin Bulanık Zaman Serisi Yaklaşımları ile Öngörüsü. İstatistik Araştırma Dergisi, 9(2), 1-11.