AYLIK BUHARLAŞMA TAHMİNİNDE YAPAY ZEKA YÖNTEMLERİNİN KULLANILABİLİRLİĞİ

Ayşe Özel; Meral Büyükyıldız

doi:10.28948/ngumuh.516891

Araştırma Makalesi

USABILITY OF ARTIFICIAL INTELLIGENCE METHODS FOR ESTIMATION OF MONTHLY EVAPORATION

Yıl 2019, Cilt: 8 Sayı: 1, 244 - 254, 28.01.2019

Ayşe Özel Meral Büyükyıldız

https://doi.org/10.28948/ngumuh.516891

Cited By: 4

Öz

Evaporation, one of the most
important components of the hydrological cycle, is influenced by many factors.
Evaporation is a meteorological parameter that is difficult to predict due to
this complex structure. In this study, the amount of monthly evaporation was
estimated using different input combinations formed by meteorological
parameters belonging to Karaman station in Konya Closed Basin. For this
purpose, Artificial Neural Networks (ANN), Support Vector Regression (SVR),
Adaptive Network Based Fuzzy Inference System (ANFIS) methods are used. As a
result, SVR has been more successful than other methods in monthly evaporation
prediction.

Anahtar Kelimeler

Evaporation, artificial neural networks, support vector regression, water resources

Kaynakça

[1] HUO, Z., FENG, S., KANG, S., DAI, X., “Artificial Neural Network Models for Reference Evapotranspiration in an Arid Area of Northwest China”, J. Arid Environ, 82, 81–90, 2012.
[2] SUDHEER, K.P., GOSAIN, A.K., RAMASASTRI, K.S., “Estimating Actual Evapotranspiration from Limited Climatic Data using Neural Computing Technique”, J. Irrig. Drain. Eng. 129 (3), 214–218, 2003.
[3] SHIRMOHAMMADI, B., VAFAKHAH, M., MOOSAVI, V., MOGHADDAMNIA, A., “Application of Several Data-Driven Techniques for Predicting Ground Water Level”, Water Resour Manag, 27(2), 419–432, 2013.
[4] KESKIN, M.E., TAYLAN, D., TERZI, O., “Adaptive Neural-Based Fuzzy Inference System (ANFIS) Approach for Modelling Hydrological Time Series”, Hydrol Sci. J., 51(4), 588–598, 2006.
[5] SHIRI, J., MARTI, P., SINGH, V.P., “Evaluation of Gene Expression Programming Approaches for Estimating Daily Evaporation through Spatial and Temporal Data Scanning”, Hydrol. Process, 28 (3), 1215–1225, 2014.
[6] TERZI, O., KESKIN, M.E., “Modelling of Daily Pan Evaporation”, J Appl Sci, 5(2), 368-372, 2005.
[7] KIM, S., SHIRI, J., KISI, O., SINGH, V.P., “Estimating Daily Pan Evaporation using Different Data Driven Methods and Lag-Time Patterns”, Water Resour. Manage., 27 (7), 2267–2286, 2013.
[8] BRUTON, J.M., MCCLENDON, R.W., HOOGENBOOM, G., “Estimating Daily Pan Evaporation with Artificial Neural Networks”, Transactions of the American Society of Agricultural Engineers, 43(2), 491-496, 2000.
[9] KUMAR, M., RAGHUWANSHI, N.S., SINGH, R., “Artificial Neural Networks Approach in Evapotranspiration Modeling: A Review”, Irrigation Science, doi:10.1007/s00271-010-0230-8, 2010.
[10] BEHMANESH, J., MEHDIZADEH, S., “Estimation of Soil Temperature using Gene Expression Programming and Artificial Neural Networks in a Semiarid Region”, Environ Earth Sci., 76 (2), 76, 2017.
[11] MEHDIZADEH, S., BEHMANESH, J., KHALILI, K., “Comparison of Artificial Intelligence Methods and Empirical Equations to Estimate Daily Solar Radiation”, J. Atmos Sol Terr Phys, 146, 215–227, 2016.
[12] BUYUKYILDIZ, M., TEZEL, G., YILMAZ, V., “Estimation of the Change in Lake Water Level by Artificial Intelligence Methods”, Water Resour Manag, 28, 4747–4763, 2014.
[13] SUDHEER, C., MAHESWARAN, R., PANIGRAHI, B.K., MATHUR S., “A Hybrid SVM-PSO Model for Forecasting Monthly Streamflow”, Neural. Comput. and Applic, 24, 1381–1389, 2014.
[14] MOHANDES, M.A., “Modeling Global Solar Radiation using Particle Swarm Optimization (PSO)”, Sol Energy, 86, 3137–3145, 2012.
[15] GOCIC, M., MOTAMEDI, S., SHAMSHIRBAND, S., PETKOVIC, D., CH, S., HASHIM, R., ARIF, M.,. “Soft Computing Approaches for Forecasting Reference Evapotranspiration”, Comput Electron Agric, 113, 164–173, 2015.
[16] TEZEL, G., BUYUKYILDIZ, M., “Monthly Evaporation Forecasting using Artificial Neural Networks and Support Vector Machines”, Theor Appl Climatol, 124,69–80, 2016.
[17] DESWAL, S., PAL, M., “Modeling of Pan Evaporation Using Support Vector Machines Algorithm”, Journal of Hydrologic Engineering, 14(1), 104-116, 2008.
[18] GOYAL, M.K., BHARTI, B., QUILTY, J., ADAMOWSKI, J., PANDEY, A., “Modeling of Daily Pan Evaporation in Sub-Tropical Climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS”, Expert Syst. Appl, 41 (11), 5267–5276, 2014.
[19] MEHDIZADEH, S., BEHMANESH, J., KHALILI, K., “Using MARS, SVM, GEP and Empirical Equations for Estimation of Monthly Mean Reference Evapotranspiration”, Computers and Electronics in Agriculture, 139, 103–114, 2017.
[20] ÖZEL, A., Meteorolojik Verileri Kullanarak Aylık Tava Buharlaşmasını Tahmin Etmek için Yapay Zeka Metotlarının Uygulanması, Yüksek Lisans Tezi, Konya Teknik Üniversitesi, Lisansüstü Eğitim Enstitüsü, Konya, 2018.
[21] SPECHT, D. F., “A General Regression Neural Network”, IEEE Transactionson Neural Networks, 2, 568-576, 1991.
[22] ALP, M., CIĞIZOĞLU, H.K., “Farklı Yapay Sinir Ağı Metodları ile Yağış Akış İlişkisinin Modellenmesi”, ITU Dergisi, 3(1), 80–88, 2004.
[23] BROOMHEAD, D., LOWE D., “Multivariable Functional Interpolation and Adaptive Networks”, Complex Systems, 2(6), 568–576, 1988.
[24] OKKAN, U., DALLKILIÇ, H.Y.,. “Radyal Tabanlı Yapay Sinir Ağları ile Kemer Barajı Aylık Akımlarının Modellenmesi”, İMO Teknik Dergi, 5957-5966, 2012
[25] PRINCIPE, J.C., EULIANO, N.R., LEFEBVRE, W.C., Neural and Adaptive Systems: Fundamentals through Simulations, John Wiley& Sons Inc., New York, 2000.
[26] HAYKIN, S., Neural Networks: A Comprehensive Foundation, 2nd edn.Prentice Hall, Upper Saddle River, 1999.
[27] JANG, J.S.R., “ANFIS: Adaptive-Network-Based Fuzzy Inference System, Systems, Man and Cybernetics, IEEE Transactions on, 23(3), 665-685, 1993,
[28] VAPNIK, V. N., The Nature of Statistical Learning Theory, Springer and Verlag, NewYork, 1995.
[29] VAPNIK, V.N., Statistical Learning Theory, John Wiley and Sons, New York, 1998.
[30] EKİCİ, S., Elektrik Güç Sistemlerinde Akıllı Sistemler Yardımıyla Arıza Tipi ve Yerinin Belirlenmesi, Doktora Tezi, Fırat Üniversitesi Fen Bilimleri Enstitüsü, Şanlı Urfa, 2007.
[31] ANONİM, Konya Kapalı Havzası Sektörel Su Tahsis Planının Hazırlanmasına Yönelik Teknik Destek Hizmet Alımı İşi, Taslak Su Talepleri Analizi Raporu, T.C. Orman ve Su İşleri Bakanlığı, Su Yönetimi Genel Müdürlüğü, 2017.