Gri Tahmin Yöntemi İle İstanbul Su Tüketiminin Modellenmesi

Yıl 2019, Cilt: 22 Sayı: 3, 755 - 761, 01.09.2019

Eyyup Ensar Başakın Mehmet Özger Necati Erdem Ünal

https://doi.org/10.2339/politeknik.422727

Cited By: 9

Öz

Mühendislik yapılarının ekonomik ve sürdürülebilir
hizmet verebilmeleri, yapım öncesi titiz bir planlama gerektirmektedir. Uzun
hizmet ömrü ve yüksek ilk yatırım maliyetleri sebebiyle ön planlama su
yapılarında ayrı bir önem taşır. Projelendirilecek su yapısının hangi boyut ve
özellikte olması gerektiği, su tüketim miktarları ile ilişkilidir. Gelecekte
gerçekleşmesi düşünülen su tüketim değerinin tespiti bu yapıların inşa sürecine
önemli katkılar sunmaktadır. Ancak bazı bölgelerde geçmişte kullanılan su miktarına
ait ölçümlerdeki yetersizlik veya eksik kayıtlar sebebiyle tahmin yapmak
zorlaşmaktadır. Klasik tahmin yöntemleri belirli bir sayının altında veri ile
sağlıklı performans sergileyemezler. Geçmiş kayıtların az olduğu durumlarda ise
Gri Tahmin yöntemi en çok başvurulan yöntemlerden biridir. Bu çalışmada, güncel
bir yöntem olan Gri Tahmin yöntemi kullanılarak İstanbul ilinin yıllık su
tüketim değerleri tahmin edilmeye çalışılmıştır. Gri Tahmin yöntemlerinin
sonuçları istatistiksel başarı kriterleri yardımıyla test edilmiş ve
hidrolojide kullanımının mümkün olduğu görülmüştür.

Anahtar Kelimeler

Gri tahmin, su tüketimi, hidroloji, zaman serisi, matematik model

Water Consumption Model Of Istanbul City By Gray Prediction Method

Öz

Engineering structures require precise planning before
its construction to provide economical and sustainable service. Due to long
service life and high initial investment costs, preliminary planning is of
particular importance for water structures. The size and type of the water
structure to be projected is highly related to the amount of water consumption
rate. The determination of water consumption rate which is considered to be
realized in the future provides an important contribution to the construction
process of these special structures. However, in some regions it is difficult
to estimate the water demand because of inadequacies or missing records in
previous water consumption rates. Classical prediction methods cannot perform
well with limited number of data. Gray Prediction Model is one of the most
commonly used methods when there are few records. In this study, annual water
consumption rates of the province of Istanbul were estimated by using Gray
Prediction Model which is a contemporary method. The results of the Gray
Prediction Model was tested with the help of statistical error criteria and it
was found out that this method gives satisfactory results in hydrology area
even if number of data is limited.

Anahtar Kelimeler

Gray prediction, water consumption, hydrology, time series, mathematical model

Kaynakça

• [1] Maidment, D. R., and Parzen, E., "Cascade model of monthly municipal water use." Water Resources Research, 20(1): 15-23, (1984)
• [2] Dziegielewski, B., and Boland, J. J., "Forecasting urban water use: the iwr-main model1." JAWRA Journal of the American Water Resources Association, 25(1): 101-109, (1989)
• [3] Altunkaynak, A., Özger, M., and Çakmakci, M., "Water consumption prediction of istanbul city by using fuzzy logic approach." Water Resources Management, 19(5): 641-654, (2005)
• [4] Altunkaynak, A., and Nigussie, T. A., "Monthly water consumption prediction using season algorithm and wavelet transform;based models." Journal of Water Resources Planning and Management, 143(6): 04017011, (2017)
• [5] Firat, M., Turan, M. E., and Yurdusev, M. A., "Comparative analysis of fuzzy inference systems for water consumption time series prediction." Journal of Hydrology, 374(3): 235-241, (2009)
• [6] Adamowski, J., and Karapataki, C., "Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ann learning algorithms." Journal of Hydrologic Engineering, 15(10): 729-743, (2010)
• [7] Nasseri, M., Moeini, A., and Tabesh, M., "Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming." Expert Systems with Applications, 38(6): 7387-7395, (2011)
• [8] Zhang, D., Ni, G., Cong, Z., Chen, T., and Zhang, T., "Statistical interpretation of the daily variation of urban water consumption in Beijing, China." Hydrological Sciences Journal, 59(1): 181-192, (2013)
• [9] Liu, Y., Zhao, J., and Wang, Z., "Identifying determinants of urban water use using data mining approach." Urban Water Journal, 12(8): 618-630, (2014)
• [10] Zhou, S. L., McMahon, T. A., Walton, A., and Lewis, J., "Forecasting daily urban water demand: a case study of Melbourne." Journal of Hydrology, 236(3): 153-164, (2000)
• [11] Trivedi, H. V., and Singh, J. K., "Application of grey system theory in the development of a runoff prediction model." Biosystems Engineering, 92(4): 521-526, (2005)
• [12] Yu, P.-S., Chen, C.-J., and Chen, S.-J., "Application of gray and fuzzy methods for rainfall forecasting." Journal of Hydrologic Engineering, 5(4): 339-345, (2000)
• [13] Ho, J.-Y., and Lee, K., "Grey forecast rainfall with flow updating algorithm for real-time flood forecasting." Water, 7(5): 1840, (2015).
• [14] Lin, Y.-H., Chiu, C.-C., Lee, P.-C., and Lin, Y.-J., "Applying fuzzy grey modification model on inflow forecasting." Engineering Applications of Artificial Intelligence, 25(4): 734-743, (2012)
• [15] Chou, C.-m., "Applying multi-resolution analysis to differential hydrological grey models with dual series." Journal of Hydrology, 332(1): 174-186, (2007)
• [16] Wang Yu-lu, Y. Y.-b., Yuan Xiao-hui , Zhang Miao "A modified nonlinear grey bernoulli model used for water consumption prediction." March 2017 Singapore International Conferences. (2017)
• [17] Nash, J. E., and Sutcliffe, J. V., "River flow forecasting through conceptual models part I — A discussion of principles." Journal of Hydrology, 10(3): 282-290, (1970)