Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir

Due to the complexity of influencing factors and the limitation of existing scientific knowledge, current monthly inflow prediction accuracy is unable to meet the requirements of various water users yet. A flow time series is usually considered as a combination of quasi-periodic signals contaminated by noise, so prediction accuracy can be improved by data preprocess. Singular spectrum analysis (SSA), as an efficient preprocessing method, is used to decompose the original inflow series into filtered series and noises. Current application of SSA only selects filtered series as model input without considering noises. This paper attempts to prove that noise may contain hydrological information and it cannot be ignored, a new method that considerers both filtered and noises series is proposed. Support vector machine (SVM), genetic programming (GP), and seasonal autoregressive (SAR) are chosen as the prediction models. Four criteria are selected to evaluate the prediction model performance: Nash–Sutcliffe efficiency, Water Balance efficiency, relative error of annual average maximum (REmax) monthly flow and relative error of annual average minimum (REmin) monthly flow. The monthly inflow data of Three Gorges Reservoir is analyzed as a case study. Main results are as following: (1) coupling with the SSA, the performance of the SVM and GP models experience a significant increase in predicting the inflow series. However, there is no significant positive change in the performance of SAR (1) models. (2) After considering noises, both modified SSA-SVM and modified SSA-GP models perform better than SSA-SVM and SSA-GP models. Results of this study indicated that the data preprocess method SSA can significantly improve prediction precision of SVM and GP models, and also proved that noises series still contains some information and has an important influence on model performance.

[1]  Anatoly A. Zhigljavsky,et al.  Analysis of Time Series Structure - SSA and Related Techniques , 2001, Monographs on statistics and applied probability.

[2]  Shie-Yui Liong,et al.  Rainfall and runoff forecasting with SSA-SVM approach , 2001 .

[3]  Bellie Sivakumar,et al.  Nonlinear dynamics and chaos in hydrologic systems: latest developments and a look forward , 2009 .

[4]  Shie-Yui Liong,et al.  FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES 1 , 2002 .

[5]  Chuntian Cheng,et al.  Using support vector machines for long-term discharge prediction , 2006 .

[6]  Qiang Zhang,et al.  Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting , 2011 .

[7]  Ozgur Kisi,et al.  Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models , 2011 .

[8]  P. C. Nayak,et al.  Fuzzy computing based rainfall–runoff model for real time flood forecasting , 2005 .

[9]  C. L. Wu,et al.  Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis , 2011 .

[10]  Hafzullah Aksoy,et al.  Artificial neural network models for forecasting monthly precipitation in Jordan , 2009 .

[11]  Jose D. Salas,et al.  Estimation of ARMA Models with seasonal parameters , 1982 .

[12]  N. Matalas.  Mathematical assessment of synthetic hydrology , 1967 .

[13]  Ju-liang Jin,et al.  Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model , 2009 .

[14]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[15]  Ali Aytek,et al.  An application of artificial intelligence for rainfall-runoff modeling , 2008 .

[16]  R. Vautard,et al.  Singular-spectrum analysis: a toolkit for short, noisy chaotic signals , 1992 .

[17]  Dawei Han,et al.  Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction , 2011 .

[18]  K. Lee,et al.  A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer , 2011 .

[19]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[20]  Yen-Chang Chen,et al.  A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction , 2001 .

[21]  Vladan Babovic,et al.  GENETIC PROGRAMMING: A NEW PARADIGM IN RAINFALL RUNOFF MODELING 1 , 2002 .

[22]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series , 2009 .

[23]  J. A. Ferreira,et al.  Singular spectrum analysis and forecasting of hydrological time series , 2006 .

[24]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[25]  Dawei Han,et al.  Identification of Support Vector Machines for Runoff Modelling , 2004 .

[26]  Alaa F. Sheta,et al.  Forecasting using genetic programming , 2001, Proceedings of the 33rd Southeastern Symposium on System Theory (Cat. No.01EX460).

[27]  Hahn-Ming Lee,et al.  Model selection of SVMs using GA approach , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

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

[29]  N. Chang,et al.  Short-term streamflow forecasting with global climate change implications – A comparative study between genetic programming and neural network models , 2008 .

[30]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[31]  K. Chau,et al.  A hybrid model coupled with singular spectrum analysis for daily rainfall prediction , 2010 .

[32]  Chuntian Cheng,et al.  Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model , 2012 .

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

[34]  I-Fan Chang,et al.  Support vector regression for real-time flood stage forecasting , 2006 .

[35]  Wenjian Wang,et al.  Determination of the spread parameter in the Gaussian kernel for classification and regression , 2003, Neurocomputing.

[36]  Peter A. Whigham,et al.  Modelling rainfall-runoff using genetic programming , 2001 .

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

[38]  Upmanu Lall,et al.  Climate teleconnections to Yangtze river seasonal streamflow at the Three Gorges Dam, China , 2007, International Journal of Climatology.

[39]  O. Kisi,et al.  Suspended sediment modeling using genetic programming and soft computing techniques , 2012 .

[40]  Gwo-Fong Lin,et al.  Effective forecasting of hourly typhoon rainfall using support vector machines , 2009 .