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Water Demand Forecast Model of Least Squares Support Vector Machine Based on Particle Swarm Optimization

Author(s): Kun Yan; Min-Zhi Yang

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Keywords: No keywords

Abstract: In order to solve the problem of precision of water demand forecast model, a coupled water demand forecast model of particle swarm optimization (PSO) algorithm and least squares support vector machine (LS-SVM) are proposed in this paper. A PSO-LSSVM model based on parameter optimization was constructed in a coastal area of Binhai, Jiangsu Province, and the total water demand in 2009 and 2010 were simulated and forecasted with the absolute value of the relative errors less than 2.1%. The results showed that the model had good simulation effect and strong generalization performance, and can be widely used to solve the problem of small- sample, nonlinear and high dimensional water demand forecast.


Year: 2018

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