An Enhanced Particle Swarm Optimization with Levy Flight for RBF Neural Network in Typical Karst Area, South China

Abstract

This paper applied an enhanced particle swarm optimization (PSO) technique with levy flight algorithm for training the radial basis function (RBF) neural network to forecast the data of runoff in the Houzhaihe River basin, a typical karst area in Guizhou Province, southwest China. The karst aquifer system is a highly nonlinear and complex system due to its unique aqueous medium, the complexity of its hydrogeological conditions makes the traditional hydrological model research results unsatisfactory, and the establishment of a physical distributed model based on hydrological mechanism requires a large number of hydrogeological parameters, which are often unavailable in karst areas. Radial Basis Function (RBF) neural network has been widely used in various fields because of its simple structure, high-speed calculation and ability to approximate any nonlinear function. Based on the RBF neural network, this paper established a time series prediction model for typical karst regions. In order to improve the performance of RBF network model, we applied an enhanced particle swarm optimization with levy flight in this research. The results show the proposed enhanced RBF model performs much better than the one without improvement by the levy flight.