Frequency Based Prediction of Büyük Menderes Flows

Frequency Based Prediction of Büyük Menderes Flows

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.

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  • [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 RainfallRunoff 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-DurationFrequency 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 RuleBased 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-RuleBased 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 FloodForecasting 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.