Research on Grey Prediction Model Based on Particle Swarm Optimization Algorithm for Predicting Lake Water Level
The water level of lakes is of great significance for ensuring regional water supply safety,flood control and disaster reduction.In order to improve the accuracy and reliability of lake water level prediction,an improved grey prediction model based on particle swarm optimization algorithm(PSO)is proposed to address the problem of decreased prediction accuracy of traditional grey prediction models when facing nonlinear and uncertain data sequences.By using particle swarm optimization algorithm to find the optimal parameter combination,improving the parameter estimation and backcalculation methods of the grey prediction model,the improved model was applied to predict lake water level.By comparing with the BP neural network model,the superiority of this research model in short-term prediction accuracy was verified.Not only does it provide a new and efficient method for predicting lake water levels,but it also broadens the path for the application of particle swarm optimization in the field of environmental science,providing valuable references for the scientific management and decision-making of lake water resources.
particle swarm optimization algorithmgrey predictionlake water levelBP neural network
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