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Yapay sinir ağları yöntemi kullanılarak buharlaşma miktarı tahmini

Year 2018, Volume: 9 Issue: 1, 543 - 551, 05.04.2018

Abstract

Buharlaşma tahmini, özellikle kurak dönemlerde ve kurak alanlarda, sulama yönetiminde ve hidrolik tasarımlarda önemli bir rol oynar. Bu çalışmada, Hargreaves-Samani, Ritchie ve Turc denklemleri gibi ampirik (geleneksel) yöntemler ile Yapay sinir ağları (YSA) yöntemi performanslarının değerlendirilmesi için buharlaşma miktarı tahmin edilmeye çalışılmıştır. Çalışma alanı olarak Massachusett, U.S.A (Cambridge Hazne ve havzası) seçilmiştir. Günlük ortalama buharlaşma miktarı tahmini için ortalama günlük hava sıcaklığı (HS), rüzgâr hızı (RH), güneşlenme miktarı (GM) ve bağıl nem (BN) kullanılmıştır. Tüm günlük veriler eğitim ve test verisi olmak üzere ikiye ayrılmıştır. YSA optimizasyonu için hataların geriye yayılma ilkesine göre çalışan geriye beslemeli ağ algoritması kullanılmıştır. YSA sonuçları, geleneksel Hargreaves-Samani, Ritchie ve Turc yöntemlerinin sonuçları ile karşılaştırılmıştır. Karşılaştırma, ANN modelinin buharlaşma miktarı tahmininde geleneksel yöntemlerden daha iyi performans ortaya koyduğu göstermiştir.

References

  • Aytek, A., Güven, A.,Yüce, M.İ., Aksoy, H., (2008). An explicit neural network formulation for evapotranspiration, Hydrological Sciences Journal, 53, 4, 893-904, DOI: 10.1623/ hysj.53.4.893.
  • Demirci, M., Baltaci, A., (2013). Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches, Neural Computing Applications, 23, 145-151.
  • Demirci, M., Üneş, F., Aköz, M.S. (2015). Prediction of cross-shore sandbar volumes using neural network approach. Journal of Marine Science and Technology 20(1), 171-179.
  • Fenga, Y., Cuib, N., Zhaob, L., Hud, X., Gonga, D., (2016). Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China, Journal of Hydrology, 536, 376–383.
  • Hargreaves, G.H., Samani, Z.A., (1985). Reference crop evapotranspiration from temperature, Applied Engineering. in Agric. 1, 96-99.
  • Jones, J. W., Ritchie, J.T., (1990). Crop growth models. Management of Farm Irrigation Systems (ed. by G. J. Hoffman, T. A. Howel & K. H. Solomon), 63–89. ASAE Monograph no.9, ASAE, St Joseph, Michigan, USA.
  • Kaya, Y. Z., Mamak, M., Unes, F., (2016). Evapotranspiration Prediction Using M5T Data Mining Method, International Journal of Advanced Engineering Research and Science (IJAERS), 3, 12, 225-229.
  • Kisi, O., (2014). Comparison of Different Empirical Methods for Estimating Daily Reference Evapotranspiration in Mediterranean Climate, Journal of Irrigation and Drainage Engineering, 140, 1.
  • Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., Pruitt, W. O., (2002). Estimating Evapotranspiration using Artificial Neural Network, Journal of Irrigation and Drainage Engineering, 128, 4.
  • Partal, T., (2016). Comparison of wavelet based hybrid models for daily evapotranspiration estimation using meteorological data, KSCE Journal of Civil Engineering, 20, 5, 2050–2058.
  • Turc L., (1961). Evaluation des besoins en eau d’irrigation, ´evapotranspiration potentielle, formulation simplifi´e et mise `a jour. Annales Agronomiques 12, 13–49.
  • USGS.gov | Science for a changing world [WWW Document], n.d. URL https://www.usgs.gov/
  • Üneş, F., (2010a). Dam reservoir level modelıng by neural network approach. A casestudy, Neural Network World, 4,10, 461–474.
  • Üneş, F., (2010b). Prediction of density flow plunging depth in dam reservoirs: an artificial neural network approach, CLEAN - Soil, Air, Water, 38, 3, 296–308, DOI: 10.1002/clen.200900238.
  • Üneş, F., (2010b). Prediction of density flow plunging depth in dam reservoirs: an artificial neural network approach, CLEAN - Soil, Air, Water, 38, 3, 296–308, DOI: 10.1002/clen.200900238.
  • Ünes, F., Yildirim, S., Cigizoglu, HK., Coskun, H. (2013). Estimation of dam reservoir volume fluctuations using artificial neural network and support vector regression, Journal of Engineering Research, 1(3), 53-74.
  • Üneş, F., Demirci, M., Kişi, Ö. (2015). Prediction of millers ferry dam reservoir level in usa using artificial neural network, Periodica Polytechnica Civil Engineering, 59(3), 309–318.
Year 2018, Volume: 9 Issue: 1, 543 - 551, 05.04.2018

Abstract

References

  • Aytek, A., Güven, A.,Yüce, M.İ., Aksoy, H., (2008). An explicit neural network formulation for evapotranspiration, Hydrological Sciences Journal, 53, 4, 893-904, DOI: 10.1623/ hysj.53.4.893.
  • Demirci, M., Baltaci, A., (2013). Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches, Neural Computing Applications, 23, 145-151.
  • Demirci, M., Üneş, F., Aköz, M.S. (2015). Prediction of cross-shore sandbar volumes using neural network approach. Journal of Marine Science and Technology 20(1), 171-179.
  • Fenga, Y., Cuib, N., Zhaob, L., Hud, X., Gonga, D., (2016). Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China, Journal of Hydrology, 536, 376–383.
  • Hargreaves, G.H., Samani, Z.A., (1985). Reference crop evapotranspiration from temperature, Applied Engineering. in Agric. 1, 96-99.
  • Jones, J. W., Ritchie, J.T., (1990). Crop growth models. Management of Farm Irrigation Systems (ed. by G. J. Hoffman, T. A. Howel & K. H. Solomon), 63–89. ASAE Monograph no.9, ASAE, St Joseph, Michigan, USA.
  • Kaya, Y. Z., Mamak, M., Unes, F., (2016). Evapotranspiration Prediction Using M5T Data Mining Method, International Journal of Advanced Engineering Research and Science (IJAERS), 3, 12, 225-229.
  • Kisi, O., (2014). Comparison of Different Empirical Methods for Estimating Daily Reference Evapotranspiration in Mediterranean Climate, Journal of Irrigation and Drainage Engineering, 140, 1.
  • Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., Pruitt, W. O., (2002). Estimating Evapotranspiration using Artificial Neural Network, Journal of Irrigation and Drainage Engineering, 128, 4.
  • Partal, T., (2016). Comparison of wavelet based hybrid models for daily evapotranspiration estimation using meteorological data, KSCE Journal of Civil Engineering, 20, 5, 2050–2058.
  • Turc L., (1961). Evaluation des besoins en eau d’irrigation, ´evapotranspiration potentielle, formulation simplifi´e et mise `a jour. Annales Agronomiques 12, 13–49.
  • USGS.gov | Science for a changing world [WWW Document], n.d. URL https://www.usgs.gov/
  • Üneş, F., (2010a). Dam reservoir level modelıng by neural network approach. A casestudy, Neural Network World, 4,10, 461–474.
  • Üneş, F., (2010b). Prediction of density flow plunging depth in dam reservoirs: an artificial neural network approach, CLEAN - Soil, Air, Water, 38, 3, 296–308, DOI: 10.1002/clen.200900238.
  • Üneş, F., (2010b). Prediction of density flow plunging depth in dam reservoirs: an artificial neural network approach, CLEAN - Soil, Air, Water, 38, 3, 296–308, DOI: 10.1002/clen.200900238.
  • Ünes, F., Yildirim, S., Cigizoglu, HK., Coskun, H. (2013). Estimation of dam reservoir volume fluctuations using artificial neural network and support vector regression, Journal of Engineering Research, 1(3), 53-74.
  • Üneş, F., Demirci, M., Kişi, Ö. (2015). Prediction of millers ferry dam reservoir level in usa using artificial neural network, Periodica Polytechnica Civil Engineering, 59(3), 309–318.
There are 17 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Bestami Taşar 0000-0003-4842-3937

Fatih Üneş 0000-0001-5751-6970

Mustafa Demirci 0000-0002-3249-2586

Yunus Ziya Kaya 0000-0002-4357-9177

Publication Date April 5, 2018
Submission Date June 17, 2017
Published in Issue Year 2018 Volume: 9 Issue: 1

Cite

IEEE B. Taşar, F. Üneş, M. Demirci, and Y. Z. Kaya, “Yapay sinir ağları yöntemi kullanılarak buharlaşma miktarı tahmini”, DUJE, vol. 9, no. 1, pp. 543–551, 2018.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456