Büyük Menderes Akımlarının Frekans Tabanlı Tahmini

Öz

Bu çalışmada, mevsimsel salınımlar gösteren, içsel bağımlı ve kaotik zaman serilerinin veriye dayalı tahmini için yeni bir yöntem önerilmektedir. Yöntem, gözlenmiş serilerdeki değer aralıklarının frekanslarına bağlı olarak mevcut verilerin zamansal ve niceliksel ilişkilerine dayalı olarak eksik veriler için tahminler üretmektedir. Frekans tabanlı tahmin adı verilen yöntem, genel bir yaklaşıma sahiptir ve test/onay/düzenleme/ağırlık katsayısı belirleme gibi işlemler yapılması gerekmemektedir. Geliştirilen yöntem, Büyük Menderes havzasındaki 34 istasyonun 9050 adet aylık toplam akım gözleminin tahmini ve 1210 adet eksik verinin tamamlanmasında kullanılmıştır. Gözlemler ve tahminler arasında tüm istasyonlar için elde edilen yüksek korelasyon değerleri, önerilen yöntemin akım verilerinin tahmininde başarılı olduğunu göstermektedir.

Anahtar Kelimeler

• Frekans tabanlı tahmin,veriye dayalı modelleme,aylık toplam akım verileri,Büyük Menderes Havzası,eksik verilerin tamamlanması

Frequency Based Prediction of Büyük Menderes Flows

Öz

In this study, a new method for the data driven prediction of interrelated and chaotic time series data showing seasonal fluctuations is proposed. The method produces predictions based on the temporal and quantitative relationships among the available data related with the frequencies of the value ranges of observed data. The method, which is called frequency based prediction, has a general approach and requires no testing/validation/adjustment/ weight determination steps. The developed method is used for predicting 9050 monthly total flow observations of 34 stations on Büyük Menderes River and for infilling 1210 missing data. High correlations obtained between the observations and predictions for all stations show that the proposed method is successful in the prediction of streamflow data.

Anahtar Kelimeler

• Frequency based prediction,data-driven modeling,monthly total streamflow data,Büyük Menderes Basin,estimation of missing data

Kaynakça

1. Solomatine, D. P., Abrahart, R. J., See, L. M., Data-Driven Modelling: Concepts, Approaches and Experiences. Practical Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications, R. J. Abrahart, L. M. See and D. P. Solomatine, (editörler), Springer, Berlin, 17-30, 2008.
2. Dawson, C. W., Wilby, R., An Artificial Neural Network Approach to Rainfall-Runoff Modelling. Hydrol. Sci. J., 43(1), 47-66, 1998.
3. Govindaraju, R. S., Ramachandra, R.A., Artificial Neural Networks in Hydrology, Kluwer, Dordrecht, 2001.
4. Tayfur, G., Singh, V. P., ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff. J. Hydraul. Eng., 132(12), 1321-1330, 2006.
5. Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., Wilby, R. L., Two Decades of Anarchy? Emerging Themes and Outstanding Challenges for Neural Network River Forecasting. Prog. Phys. Geogr., 36(4), 480-513, 2012.
6. Huo, Z., Feng, S., Kang, S., Huang, G., Wang, F., Guo, P., Integrated Neural Networks for Monthly River Flow Estimation in Arid Inland Basin of Northwest China. J. Hydrol., 420-421, 159-170, 2012.
7. Bhattacharya, B., Van Kessel, T., Solomatine, D. P., Spatio-Temporal Prediction of Suspended Sediment Concentration in the Coastal Zone Using Artificial Neural Network and a Numerical Model. J. Hydroinform., 14(3), 574-594, 2012.
8. Adamala, S., Raghuwanshi, N. S., Mishra, A., Tiwari, M. K. Evapotranspiration Modeling Using Second-Order Neural Networks. J. Hydrol. Eng., 19(6), 1131-1140, 2014.

9. Mirhosseini, G., Srivastava, P., Fang, X., Developing Rainfall Intensity-Duration-Frequency Curves for Alabama Under Future Climate Scenarios Using Artificial Neural Networks. J. Hydrol. Eng., 10.1061/(ASCE)HE.1943-5584.0000962 , 04014022, 2013.
10. Pesti, G., Shrestha, B. P., Duckstein, P., Bogárdi, I. A., Fuzzy Rule-Based Approach to Drought Assessment. Water Resour. Res., 32(6), 1741-1747, 1996.
11. Abebe, A. J., Solomatine, D. P., Venneker, R., Application of Adaptive Fuzzy Rule-Based Models for Reconstruction of Missing Precipitation Events. Hydrol. Sci. J., 45 (3), 425-436, 2000.
12. Bogardi, I., Duckstein, L., Pongracz, R., Galambosi, A., Experience With Fuzzy-Rule-Based Modeling of Hydrological Extremes. Proc., Risk-Based Decisionmaking in Water Resources IX, ASCE, Reston, VA, 44-60, 2001.
13. Vernieuwe, H., Georgieva, O., De Baets, B., Pauwels, V. R. N., Verhoest, N. E. C., De Troch, F. P., Comparison of Data-Driven Takagi–Sugeno Models of Rainfall–Discharge Dynamics. J. Hydrol., 302(1-4), 173-186, 2005.
14. Nayak, P. C., Explaining Internal Behavior in a Fuzzy If-Then Rule-Based Flood-Forecasting Model. J. Hydrol. Eng., 15(1), 20-28, 2010.
15. Bray, M., Han, D., Identification of Support Vector Machines for Runoff Modelling. J. Hydroinf., 6, 265-280, 2004.
16. Chen, X., Zhu, S., Improved Hybrid Model Based on Support Vector Regression Machine for Monthly Precipitation Forecasting. J. Computers, 8(1), 232-239, 2013.
17. Lin, J. Y., Cheng, C. T., Chau, K. W., Using Support Vector Machines for Long-Term Discharge Prediction. Hydrol. Sci. J., 51(4), 599-612, 2006.
18. Kişi, Ö., Çimen, M., A Wavelet-Support Vector Machine Conjunction Model for Monthly Streamflow Forecasting. J. Hydrol., 399(1-2), 132-140, 2011.
19. Karahan, H., İplikçi, S., Gürarslan, G., River Flow Estimation From Upstream Flow Records Using Support Vector Machines, J. Appl. Math., 2014
20. Solomatine, D. P., Shrestha, D. L., Maskey, M., Instance Based Learning Compared to Other Data-Driven Methods in Hydrological Forecasting. Hydrol. Process., 22, 275-287, 2007.
21. Yılmaz, A. G., Muttil, N., Runoff Estimation by Machine Learning Methods and Application to the Euphrates Basin in Turkey. J. Hydrol. Eng., 19(5), 1015-1025, 2014.
22. Mukerji, A., Chatterjee,C., Raghuwanshi, N. S., Flood Forecasting Using ANN, Neuro-Fuzzy, and Neuro-GA Models. J. Hydrol. Eng., 14(6), 647-652, 2009.
23. Elshorbagy, A., Corzo. G., Srinivasulu, S., Solomatine, D. P., Experimental Investigation of the Predictive Capabilities of Data Driven Modeling Techniques in Hydrology - Part 1: Concepts and methodology. Hydrol. Earth Syst. Sci., 14, 1931-1941, 2010.