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.
Highlights
Water demand forecast is an important part of water supply system optimal operation
Because artificial neural network is a method following the principle of empirical risk minimization, the generalization performance of its model is much worse than that of support vector machine which follows the principle of structural risk minimization when dealing with small sample problems [3]
Support vector machine and its improved models are widely used to solve the problems of small sample, nonlinear and high dimensional water demand forecast
Summary
Water demand forecast is an important part of water supply system optimal operation. Accurate water demand forecast can allocate limited water resources reasonably and effectively, it can avoid the waste of resources caused by wrong water allocation, and ease the tension of water resources to a large extent. Because of the late start of water demand forecast in our country [1], the series length of water demand data is short and the reliability of data is low [2], and there are many factors influencing water demand forecast, such as quota method, time series method, trend analysis method and other traditional forecast methods They cost a large amount of work, and difficult to guarantee the accuracy of the forecast. Because artificial neural network is a method following the principle of empirical risk minimization, the generalization performance of its model is much worse than that of support vector machine which follows the principle of structural risk minimization when dealing with small sample problems [3]. Support vector machine and its improved models are widely used to solve the problems of small sample, nonlinear and high dimensional water demand forecast
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