Abstract
This study develops river stage forecasting models combining Support Vector Regression (SVR) and optimization algorithms. The SVR is applied for forecasting river stage, and the optimization algorithms, including Grid Search (GS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC), are applied for searching the optimal parameters of the SVR. For assessing the applicability of models combining SVR and optimization algorithms, the model performance is compared with ANN and ANFIS models. In terms of model efficiency, SVR-GS, SVR-GA, SVR-PSO and SVR-ABC models yield better results than ANN and ANFIS models. SVR-PSO and SVR-ABC models produce relatively better efficiency than SVR-GS and SVR-GA models. SVR-PSO and SVR-ABC yield the best performance in terms of model efficiency. Results indicate that river stage forecasting models combining SVR and optimization algorithms can be used as an effective tool for forecasting river stage accurately.
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References
Seo, Y., Kim, S., Kisi, O., Singh, V.P.: Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. J. Hydrol. 520, 224–243 (2015)
Vapnik, V.N.: Statistical learning theory. Wiley, New York (1998)
Noori, R., Karbassi, A.R., Moghaddamnia, A., Han, D., Zokaei-Ashtiani, M.H., Farokhnia, A., Ghafari Gousheh, M.: Assessment of input variables determination on the SVM model performance using PCA, Gamma test and forward selection techniques for monthly stream flow prediction. J. Hydrol. 401, 177–189 (2011)
Rao, S.S.: Engineering optimization: theory and practice, 4th edn. John Wiley & Sons Inc., Hoboken (2009)
Goldberg, D.: Genetic algorithms in search, optimization, and machine learning, 1st edn. Addison-Wesley, Boston (1989)
Yuan, F.C.: Parameters optimization using genetic algorithms in support vector regression for sales volume forecasting. Appl. Math. 3(10A), 1480–1486 (2012)
Kaltech, A.M.: Wavelet genetic algorithm-support vector regression (Wavelet GA-SVR) for monthly flow forecasting. Water Resour. Manage. 29(4), 1283–1293 (2015)
Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Proceedings of the 2007 IEEE Swarm Intelligence Symposium, Honolulu, H.I., USA, pp. 120–127 (2007)
Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)
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© 2016 Springer-Verlag Berlin Heidelberg
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Seo, Y., Kim, S., Singh, V.P. (2016). Physical Interpretation of River Stage Forecasting Using Soft Computing and Optimization Algorithms. In: Kim, J., Geem, Z. (eds) Harmony Search Algorithm. Advances in Intelligent Systems and Computing, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47926-1_25
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DOI: https://doi.org/10.1007/978-3-662-47926-1_25
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