A data-driven model based on Fourier transform and support vector regression for monthly reservoir inflow forecasting

The recent trend for data-driven streamflow forecasting is to hybridize artificial intelligence with decomposition pre-processing. In this paper, a decomposition-based data-driven model called FT-SVR that exploits both Fourier transform (FT) and support vector regression (SVR) techniques is proposed for monthly reservoir inflow forecasting and the Three Gorges Dam (TGD) located on the Yangtze River in China is taken as the case for study. As the inflow time series contains oscillations of disparate scales, FT-SVR uses FT to appropriately decompose the series into multiple decomposed components, with each component comprising of neighboring frequencies and having a clear physical meaning. SVR is employed to develop an independent forecasting model for each decomposed component. The development of each SVR model involves data normalization, input selection based on autocorrelation function and partial autocorrelation function analysis, and parameter calibration by a metaheuristic. FT-SVR is compared with three other models which are the same with FT-SVR except that one uses ensemble empirical mode decomposition, one uses singular spectrum analysis for decomposition, and the other one performs no decomposition. Experimental results demonstrate that FT-SVR is able to give almost perfect monthly inflow forecasting for the TGD and significantly outperforms the three other models, in terms of evaluation criteria including root mean squared error, correlation coefficient, mean average percentage error, Nash-Sutcliffe efficiency coefficient, and relative error of maximum/minimum monthly inflow.

[1]  R. Stouffer,et al.  Stationarity Is Dead: Whither Water Management? , 2008, Science.

[2]  Ting Zhou,et al.  Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression , 2014, Water Resources Management.

[3]  Shenglian Guo,et al.  Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir , 2014, Stochastic Environmental Research and Risk Assessment.

[4]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[5]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[6]  K. Budu,et al.  Comparison of Wavelet-Based ANN and Regression Models for Reservoir Inflow Forecasting , 2014 .

[7]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[8]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[9]  V. Singh,et al.  Evaluation of flood frequency under non-stationarity resulting from climate indices and reservoir indices in the East River basin, China , 2015 .

[10]  K. Chau,et al.  Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques , 2009 .

[11]  Yufeng Ren,et al.  Monthly Mean Streamflow Prediction Based on Bat Algorithm-Support Vector Machine , 2016 .

[12]  Ji Chen,et al.  A service-oriented architecture for ensemble flood forecast from numerical weather prediction , 2015 .

[13]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[14]  A. Rao,et al.  Testing Hydrologic Time Series for Stationarity , 2002 .

[15]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[16]  Ozgur Kisi,et al.  Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering , 2015, Water Resources Management.

[17]  S. Liong,et al.  EC-SVM approach for real-time hydrologic forecasting , 2004 .

[18]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[19]  R. Bracewell.  The Fourier Transform and Its Applications , 1966 .

[20]  R. Noori,et al.  Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte‐Carlo simulation , 2014 .

[21]  K. P. Sudheer,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[22]  Gwo-Fong Lin,et al.  An RBF network with a two-step learning algorithm for developing a reservoir inflow forecasting model , 2011 .

[23]  Ashu Jain,et al.  Hybrid neural network models for hydrologic time series forecasting , 2007, Appl. Soft Comput..

[24]  S. S. Shen,et al.  A confidence limit for the empirical mode decomposition and Hilbert spectral analysis , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[25]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[26]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence , 2009 .

[27]  Jingjing Xie,et al.  Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models , 2016 .

[28]  Mahesh Pal,et al.  Performance evaluation of artificial neural network approaches in forecasting reservoir inflow , 2012 .

[29]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[30]  C. Loan.  Computational Frameworks for the Fast Fourier Transform , 1992 .

[31]  R. McCuen,et al.  A nonstationary flood frequency analysis method to adjust for future climate change and urbanization , 2012 .

[32]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[33]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[34]  Xiang Yu,et al.  Enhanced comprehensive learning particle swarm optimization , 2014, Appl. Math. Comput..

[35]  Pu Wang,et al.  Additive Model for Monthly Reservoir Inflow Forecast , 2015 .