Annual runoff forecast for Danjiangkou based on PSO-SVR

At present, the methods of annual runoff forecast for Danjiangkou reservoir mainly include physical statistical approach and artificial neural network (ANN) . However, these methods have the disadvantages of low accuracy and low stability. In this paper, we applied the regression support vecto rmachine (SVR) model to the annual runoff forecast for Danjiangkou Reservoir. Considering that the penalty coefficient C, the kernel parameterσ, and the insensitive loss coefficient ε all require a large amount of calculation and it is difficult to obtain their optimal value in the actual assignment process, we added the particle swarm optimization (PSO) algorithm to the SVR model and established a PSO-SVR model to realize the automatic optimization of parameters. The results showed that the PSO-SVR model has higher prediction accuracy compared with the SVR model, and has better stability and reliability than the ANN model. The model has a good application value, and can provide some reference for the development of the operation scheme of the middle route of the South-to-North Water Transfer Project.

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