A New PSO-RBF Model for Groundwater Quality Assessment

Abstract:

There are three adjustable parameters in the radial basis function, the center of the basis function cj, the width parameter and the output unit weight wj. Through optimization the parameters of the radial basis function by Particle swarm optimization algorithm, a neural network model of underground water is generated, which is used to study the grade of underground water in the ten monitoring points of the black dragon hole. By applying the PSO-RBF model to underground water assessment in the ten monitoring points of the black dragon hole, the results of this evaluation, which correspond with the real conditions, are basically in accord with those obtained by other evaluation methods, and also show the practicability to groundwater quality assessment.

• Periodical: Advanced Materials Research (Volumes 463-464)
• Pages: 922-925
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.463-464.922
• Online since: February 2012
• Authors: Bi Zhang, Jia Yang Wang, Su Lan Zhang
• Keywords: Evaluation Model, Particle Swarm Optimization Algorithm (PSO), Radial Basis Function (RBF), Underground Water

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