Monthly-scale runoff forecast model based on PSO-SVR

Abstract

Abstract The current methods used in the Lubbog reservoir runoff forecast generally have shortcomings such as low forecast accuracy and low stability. Aiming at these problems, this paper constructs a PSO-SVR mid-and-long term forecast model, and it uses the particle swarm optimization algorithm (PSO) to find the penalty coefficient C, the insensitivity coefficient ε and the gamma parameter of the Gaussian radial basis kernel function of the support vector regression machine (SVR). The results demonstrates that the average relative errors of the PSO-SVR forecast model is relatively small, which are all within a reasonable range; the qualification rates for most monthly forecasts are above 80%. Experimental results indicate that compared with multiple regression analysis, the PSO-SVR model has a higher forecast accuracy, a stronger stability, and a higher credibility. It has a certain practical value and provides a reference for related research.