Articles | Volume 368
Proc. IAHS, 368, 162–167, 2015
https://doi.org/10.5194/piahs-368-162-2015
Proc. IAHS, 368, 162–167, 2015
https://doi.org/10.5194/piahs-368-162-2015

  06 May 2015

06 May 2015

Optimal parameters of the SVM for temperature prediction

X. Shi, Q. Huang, J. Chang, Y. Wang, J. Lei, and J. Zhao X. Shi et al.
  • State Key Laboratory Base of Eco-Hydraulic Engineering in Arid Area, Xi’an University of Technology, Xi’an, 710048, P.R. China

Keywords: Temperature prediction, SVM, genetic algorithm, particle swarm optimization, developed genetic algorithm

Abstract. This paper established three different optimization models in order to predict the Foping station temperature value. The dimension was reduced to change multivariate climate factors into a few variables by principal component analysis (PCA). And the parameters of support vector machine (SVM) were optimized with genetic algorithm (GA), particle swarm optimization (PSO) and developed genetic algorithm. The most suitable method was applied for parameter optimization by comparing the results of three different models. The results are as follows: The developed genetic algorithm optimization parameters of the predicted values were closest to the measured value after the analog trend, and it is the most fitting measured value trends, and its homing speed is relatively fast.

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