Stage and Discharge Forecasting by SVM and ANN Techniques

In this study, forecasting of stage and discharge was done in a time-series framework across three time horizons using three models: (i) persistence model, (ii) feed-forward neural network (FFNN) model, and (iii) support vector machine (SVM) model. For these models, lagged values of the time series constituted the set of input variables which had been selected by principal component analysis (PCA). Parameters of FFNN and SVM models were determined by sensitivity analysis. All the three models were evaluated using data from Mahanadi River, India, and their forecasting performance was then compared. It is shown that over a shorter forecasting horizon, it is difficult to outperform the persistence model. Moreover, results show that forecasting of stage and discharge over a longer time frame by the SVM model is more accurate than that by the other two models.

[1]  M. K. Soni,et al.  Artificial Neural Network-Based Peak Load Forecasting Using Conjugate Gradient Methods , 2002, IEEE Power Engineering Review.

[2]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[3]  Momcilo Markus,et al.  PRECIPITATION-RUNOFF MODELING USING ARTIFICIAL NEURAL NETWORKS AND CONCEPTUAL MODELS , 2000 .

[4]  Ayman Ibrahim,et al.  Hysteresis Sensitive Neural Network for Modeling Rating Curves , 1997 .

[5]  Mahesh Pal,et al.  Estimation of Discharge and End Depth in Trapezoidal Channel by Support Vector Machines , 2007 .

[6]  M. McKee,et al.  SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES 1 , 2006 .

[7]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[8]  Mehdi Teimouri,et al.  Comparison of Neural Network and K-Nearest Neighbor Methods in Daily Flow Forecasting , 2010 .

[9]  Jason Smith,et al.  Neural-Network Models of Rainfall-Runoff Process , 1995 .

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

[11]  François Anctil,et al.  Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting , 2010 .

[12]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[13]  Ozgur Kisi,et al.  River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches , 2012, Water Resources Management.

[14]  A. W. Minns,et al.  Artificial neural networks as rainfall-runoff models , 1996 .

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

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

[17]  Mahesh Pal,et al.  Application of support vector machines in scour prediction on grade-control structures , 2009, Eng. Appl. Artif. Intell..

[18]  Sharad K. Jain,et al.  Setting Up Stage-Discharge Relations Using ANN , 2000 .

[19]  B. Bhattacharya,et al.  Application of artificial neural network in stage-discharge relationship , 2000 .

[20]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[21]  R. B. Rezaur,et al.  River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia , 2012, Water Resources Management.

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

[23]  Ashu Jain,et al.  River Flow Prediction Using an Integrated Approach , 2009 .

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

[25]  S. Jain,et al.  Radial Basis Function Neural Network for Modeling Rating Curves , 2003 .

[26]  MohammadSajjad Khan,et al.  Application of Support Vector Machine in Lake Water Level Prediction , 2006 .

[27]  R. E. Abdel-Aal,et al.  Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks , 2009 .