Abstract
Runoff forecasting which subjects to model pattern and parameter optimization, has an important significance of reservoir scheduling and water resources management decision-makings. This paper proposed a new forecasting model coupled phase space reconstruction technology with relevance vector machine, and its model parameters is optimized by an improved PSO algorithm. The monthly runoff time series from 1953 to 2003 at Manwan station is selected as an example. The results show that the improved PSO has efficient optimization performance and the proposed forecasting model could obtain higher prediction accuracy.
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References
Shi, Y.Z., Zhou, H.C.: Research on monthly flow uncertain reasoning model based on cloud theory. Sci. China Tech. Sci. 53, 2408–2413 (2010)
Sivakumar, B., Jayawardena, A.W., Fernando, T.M.K.G.: River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. Journal of Hydrology 265, 225–245 (2002)
Yu, X.Y., Liong, S.Y., Babovic, V.: EC-SVM approach for real time hydrologic forecasting. Journal of Hydroinformatics 6(3), 209–223 (2004)
Tipping, M.E.: The relevance vector machine. Advances in Neural Information Processing System 12, 652–658 (2000)
Agarwal, A., Triggs, B.: 3D human pose from silhouettes by relevance vector regression. Computer Vision and Pattern Recognition 2, 882–888 (2004)
Bowd, C., Medeiros, F.A., et al.: Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements. Investigative Ophthalmology & Visual Science 46, 1322–1329 (2005)
Chen, S., Gunn, S.R., Harris, C.J.: The relevance vector machine technique for channel equalization application. IEEE Trans on Neural Networks 12(6), 1529–1532 (2002)
Kantz, H., Schreiber, T.: Nonlinear time series analysis. Cambridge University Press, Cambridge (1997)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc IEEE Conf. on Neural Networks, pp. 1942–1948. IEEE Press, Piscataway (1995)
Shi, Y., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE Press (1998)
Clerc, M., Kennedy, J.: The Particle Swarm: Explosion, Stability, and Convergence in a Multi-dimensional Complex Space. IEEE Transactions on Evolutionary Computation 6, 58–73 (2002)
Eberhart, R.C., Shi, Y.: Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 84–88 (2000)
Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters 85(6), 317–325 (2003)
Jiang, H.M., Xie, K., Ren, C., et al.: A Novel Particle Swarm Optimization with Stochastic Stagnation. Journal of Sichuan University (Engineering Science Edtion) 38(4), 118–121 (2006)
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Shi, Y., Liu, H., Fan, M., Huang, J. (2013). Parameter Identification of RVM Runoff Forecasting Model Based on Improved Particle Swarm Optimization. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_19
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DOI: https://doi.org/10.1007/978-3-642-38703-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38702-9
Online ISBN: 978-3-642-38703-6
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