Refined Rainfall Prediction Using the RR-DP-PSO Algorithmic Model

Aiming at the problem of low accuracy of traditional algorithms for rainfall prediction, this paper proposes an innovative RR-DP-PSO algorithm model for rainfall prediction. By combining Ridge regression, PSO and DP-PSO algorithms, the algorithm is able to extract features from rainfall data and make effective predictions. The experimental results on the rainfall dataset in Baiyun area of Guangdong Province show that the RR-DP-PSO algorithm exhibits obvious advantages in rainfall prediction. Compared to traditional methods SVM, LSTM, and ANN, the RR-DP-PSO algorithm exhibits lower MSE,higher accuracy 86.67%, and more accurate prediction. This algorithm utilises multiple techniques in combination with each other in order to effectively improve the accuracy and reliability of rainfall prediction.Successful experiments with the RR-DP-PSO algorithm have demonstrated its potential applications in the fields of meteorology and natural disaster warning, and provided new ideas and methods for future improvements in meteorological prediction models.