¹College of Computer and Information Engineering, Guangxi Teachers Education University, Nanning, China
²Department of Mathematics and computer science, Guangxi Liuzhou Teachers college, Liuzhou, China
³Key Lab of Scientific Computing &Intelligent Information Processing in Universities of Guangxi, Guangxi Teachers Education University, Nanning, China
⁴Key Lab of Beibu Gulf Environment Change and Resource Use of ministry of Education, Guangxi Teachers Education University, Nanning, China

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Abstract—To resolve the difficulty of parameter estimation of Storm rainfall intensity formulation, a new multicellular GEP parameter estimation algorithm, named MC_GEP_MPO, with a novel individual coding scheme based on Gene Expression Programming algorithm is proposed in this paper. MC_GEP_MPO is used for solving the parameter estimation problem of the single return period of rainfall intensity forecast model using historical rainfall statistical data as a learning example. And its effectiveness in real compute instance has been evaluated. The compared experiment result shows that the proposed method exploring for parameter estimation of Storm rainfall intensity formulation is feasible and precise.

Index Terms—Multicellular Gene Expression Programming, Multi-parameter optimization. Storm rainfall intensity, parameter estimation

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Cite: Yuzhong Peng, Jie Li, Chang’an Yuan, Baoqin Hu, "Parameter Estimation Algorithm for Storm Intensity Model with Single Return Period Based on Multicellular Gene Expression Programming," Journal of Computers vol. 9, no. 9, pp. 2132-2138, 2014.