A hybrid SVM-PSO model for forecasting monthly streamflow

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...

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Bibliographic Details
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
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-013-1341-y