A hybrid support vector regression framework for streamflow forecast

Abstract Monthly streamflow time series are highly non-linear. How to improve forecast accuracy is a great challenge in hydrological studies. A lot of research has been conducted to address the streamflow forecasting problem, however, few methods are developed to make a systematic research. The objective of this study is to understand the underlying trend of streamflow so that a regression model can be developed to forecast the flow volume. In this paper, a hybrid streamflow forecast framework is proposed that integrates factor analysis, time series decomposition, data regression, and error suppression. Correlation coefficients between the current streamflow and the streamflow with lags are analyzed using autocorrelation function (ACF), partial autocorrelation function (PACF), and grey correlation analysis (GCA). Support vector regression (SVR) and generalized regression neural network (GRNN) models are integrated with seasonal and trend decomposition to make monthly streamflow forecast. Auto-regression and multi-model combination error correction methods are used to ensure the accuracy. In our experiments, the proposed method is compared with a stochastic autoregressive integrated moving average (ARIMA) streamflow forecast model. Fourteen models are developed, and the monthly streamflow data of Shigu and Xiangjiaba, China from 1961 to 2009 are used to evaluate our proposed method. Our results demonstrate that the integrated model of grey correlation analysis, Seasonal-Trend Decomposition Procedure Based on Loess (STL), Support Vector Regression (GCA-STL-SVR) exhibits an improved performance for monthly streamflow forecast. The average error of the proposed model is reduced to less than one-tenth in contrast to the state-of-the-art method and the standard deviation is also reduced by more than 30%, which implies a greater consistency.

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

[2]  Rajib Maity,et al.  Potential of support vector regression for prediction of monthly streamflow using endogenous property , 2010 .

[3]  Mohamed Abouelenien,et al.  A multi-class boosting method for learning from imbalanced data , 2016, Int. J. Granul. Comput. Rough Sets Intell. Syst..

[4]  Lei Ye,et al.  Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting , 2015, Water Resources Management.

[5]  Asaad Y. Shamseldin,et al.  A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi–Sugeno fuzzy system , 2001 .

[6]  Vijay P. Singh,et al.  Entropy-based derivation of generalized distributions for hydrometeorological frequency analysis , 2018 .

[7]  Kwok-Wing Chau,et al.  Data-driven models for monthly streamflow time series prediction , 2010, Eng. Appl. Artif. Intell..

[8]  O. Kisi.  Wavelet Regression Model as an Alternative to Neural Networks for River Stage Forecasting , 2011 .

[9]  C S P Ojha,et al.  Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India. , 2013, Water science and technology : a journal of the International Association on Water Pollution Research.

[10]  Lu Chen,et al.  Bayesian Technique for the Selection of Probability Distributions for Frequency Analyses of Hydrometeorological Extremes , 2018, Entropy.

[11]  J. Suykens,et al.  Chaos control using least squares support vector machines , 1999 .

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  Zhiyong Liu,et al.  Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting , 2014 .

[14]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[15]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Paulin Coulibaly,et al.  Improving Daily Reservoir Inflow Forecasts with Model Combination , 2005 .

[17]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

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

[19]  Lijun Xie,et al.  A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data , 2018, Pattern Recognit..

[20]  Shenglian Guo,et al.  Real-time error correction method combined with combination flood forecasting technique for improving the accuracy of flood forecasting , 2015 .

[21]  Hossein Bonakdari,et al.  Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model , 2017, Stochastic Environmental Research and Risk Assessment.

[22]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[23]  A. Kalteh.  Wavelet Genetic Algorithm-Support Vector Regression (Wavelet GA-SVR) for Monthly Flow Forecasting , 2015, Water Resources Management.

[24]  Lu Chen,et al.  Determination of Input for Artificial Neural Networks for Flood Forecasting Using the Copula Entropy Method , 2014 .

[25]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[26]  Holger R. Maier,et al.  Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river , 2005 .

[27]  Irma J. Terpenning,et al.  STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .

[28]  Jun Guo,et al.  Monthly streamflow forecasting based on improved support vector machine model , 2011, Expert Syst. Appl..

[29]  Ozgur Kisi,et al.  A wavelet-support vector machine conjunction model for monthly streamflow forecasting , 2011 .

[30]  Demetris Koutsoyiannis,et al.  Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes , 2019, Stochastic Environmental Research and Risk Assessment.

[31]  Yuqiong Liu,et al.  Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework , 2007 .

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

[33]  Che-Hao Chang,et al.  Real-time correction of water stage forecast during rainstorm events using combination of forecast errors , 2012, Stochastic Environmental Research and Risk Assessment.

[34]  Demetris Koutsoyiannis,et al.  Predictability of monthly temperature and precipitation using automatic time series forecasting methods , 2018, Acta Geophysica.

[35]  Jesús Rojo,et al.  Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing , 2017, International Journal of Biometeorology.

[36]  Andrew Hughes,et al.  Use of seasonal trend decomposition to understand groundwater behaviour in the Permo-Triassic Sandstone aquifer, Eden Valley, UK , 2016, Hydrogeology Journal.

[37]  Xueqing Zhang,et al.  A data-driven model based on Fourier transform and support vector regression for monthly reservoir inflow forecasting , 2018 .

[38]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[39]  A. Shamseldin,et al.  Methods for combining the outputs of different rainfall–runoff models , 1997 .

[40]  Holger R. Maier,et al.  Input determination for neural network models in water resources applications. Part 1—background and methodology , 2005 .