Makine Öğrenimi Kullanarak Aylık Akarsu Akışı Tahmini

Yıl 2020, Cilt: 13 Sayı: 3, 1242 - 1251, 31.12.2020

Fatih Tosunoğlu , Sinan Hanay , Emre Çintaş , Barış Özyer

https://doi.org/10.18185/erzifbed.780477

Cited By: 3

Öz

Nehir akımı tahmini herhangi bir havzadaki su kaynaklarının planlanması, dizaynı ve yönetiminde oldukça önemli rol oynamaktadır. Doğru nehir akımı tahmini su kaynakları sistemlerinin teknik ve ekonomik açıdan daha yararlı tasarlanmasını sağlamaktadır. Bu çalışmada, farklı makine öğretisi algoritmaları Çoruh havzasındaki aylık nehir akımlarının modellenmesinde kullanılmıştır. Bu amaç için, Destek Vektör Makineleri (SVM), Adaptif Yükseltme (AdaBoost), K En Yakın Komşular (KNN) ve Rassal Ormanlar ve makine öğretisi algoritmaları kullanılmış ve karşılaştırılmıştır. Kullanılan modellere ait test skoru sonuçlarına göre Random Forest based modeli diğer modellere göre daha iyi sonuç vermiştir.

Anahtar Kelimeler

Adaptif yükseltme, destek vektör makineleri, KNN, makine öğrenmesi

Monthly Streamflow Forecasting Using Machine Learning

Öz

Streamflow forecasting holds a vital role in planning, design, and management of basin water resources. Accurate streamflow forecast provides a more efficient design of water resources systems technically and economically. In this study, various machine learning algorithms were used to model monthly streamflow data in the Coruh river basin, Turkey. For modeling, Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), K-Nearest Neighbours (KNN) and Random Forest algorithms were considered and compared. Based on the test scores of the considered models with the hyperparameters, Random Forest based model outperforms all other models.

Anahtar Kelimeler

Adaptive boosting, KNN, machine learning, support vector machines

Kaynakça

• Kişi, Ö., 2004. “River Flow Modeling Using Artificial Neural Networks”. Journal of Hydrologic Engineering, 9(1), 60–63.
• Ahmed, J.A. and Sarma, A.K., 2007. “Artificial neural network model for synthetic streamflow generation”. Water Resources Management, 21(6), 1015–1029.
• Mehr, A.D., Kahya, E., Şahin, A. and Nazemosadat, M.J., 2015. “Successive-station monthly streamflow prediction using different artificial neural network Algorithms”. International Journal of Environmental Science and Technology, 12(7), 2191–2200.
• Elganiny, M.A. and Eldwer, A.E., 2018. “Enhancing the Forecasting of Monthly Streamflow in the Main Key Stations of the River Nile Basin”. Water Resources, 45(5) 660–671.
• Kisi, O. and Cigizoglu, K.H., 2007. “Comparison of different ANN techniques in river flow prediction”. Civil Engineering and Environmental Systems, 24(3) 211–231.
• Demirel, M.C., Venancio, A. and Kahya, E., 2009. “Flow forecast by SWAT model and ANN in Pracana basin”. Portugal. Advances in Engineering Software, 40(7) 467–473.
• Can, İ., Tosunoğlu, F. and Kahya, E., 2012. “Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of Çoruh basin, Turkey”. Water and Environment Journal, 26(4) 567–576.
• Wijngaard, J.B., Klein, A.M. and Können, G.P., 2003. “Homogeneity of 20th century European daily temperature and precipitation series”. International Journal of Climatology, 23(6) 679–692.
• Khaliq, M.N. and Ouarda, T.B.M.J., 2007. “On the critical values of the standard normal homogeneity test (SNHT)”. International Journal of Climatology, 27(5) 681–687.
• Yaseen, Z.M., Jaafar, O., Deo, R.C., Kisi, O., Adamowski, J., Quilty, J. and El-Shafie, A., 2016. “Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq”. Journal of Hydrology, 542, 603–614.
• Yerdelen, C., Karimi, Y. and Kahya, E., 2010. “Frequency analysis of mean monthly stream flow in Coruh basin, Turkey”. Fresenius Environmental Bulletin, 19(7), 1300–1311.
• Tosunoglu, F., Can, I. and Kahya, E., 2018. “Evaluation of spatial and temporal relationships between large-scale atmospheric oscillations and meteorological drought indexes in Turkey”. International Journal of Climatology, 38(12) 4579–4596.
• Boser, B.E., Guyon, I.M. and Vapnik, V.N., 1992. “A training algorithm for optimal margin classifiers”. Proceedings of the fifth annual workshop on Computational learning theory - COLT ’92.
• Cortes, C. and Vapnik, V., 1995. “Support-vector networks”. Machine Learning, 20(3), 273–297.
• Freund, Y. and Schapire, R.E., 1995. “A desicion-theoretic generalization of on-line learning and an application to boosting”. Lecture Notes in Computer Science, 23–37.
• Freund, Y. and Schapire, R.E., 1997. “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting”. Journal of Computer and System Sciences, 55(1), 119–139.
• Ho, T.K., 1998. “The random subspace method for constructing decision forests”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844.