A hybrid SVM-PSO model for forecasting monthly streamflow

• Published in: Neural computing & applications 2014-05, Vol.24 (6), p.1381-1389
• Main Authors: Sudheer, Ch, Maheswaran, R., Panigrahi, B. K., Mathur, Shashi
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Connectionism. Neural networks; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Earth sciences; Earth, ocean, space; Exact sciences and technology; Hydrology; Hydrology. Hydrogeology; Image Processing and Computer Vision; Inference from stochastic processes; time series analysis; Mathematics; Memory organisation. Data processing; Original Article; Probability and statistics; Probability and Statistics in Computer Science; Sciences and techniques of general use; Software; Statistics
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-013-1341-y

The long-term streamflow forecasts are very significant in planing and reservoir operations. The streamflow forecasts have to deal with a complex and highly nonlinear data patterns. This study employs support vector machines (SVMs) in predicting monthly streamflows. SVMs are proved to be a good tool for forecasting the nonlinear time series. But the performance of the SVM depends solely upon the appropriate choice of parameters. Hence, particle swarm optimization technique is employed in tuning SVM parameters. The proposed SVM-PSO model is used in forecasting the streamflow values of Swan River near Bigfork and St. Regis River near Clark Fork of Montana, United States. Further SVM model with various input structures is constructed, and the best structure is determined using various statistical performances. Later, the performance of the SVM model is compared with the autoregressive moving average model (ARMA) and artificial neural networks (ANN's). The results indicate that SVM could be a better alternative for predicting monthly streamflows as it provides a high degree of accuracy and reliability.