ARIMA-KOA-CNN-GRU-Attention Model for Improving GNSS Water Vapor Prediction
Experimental results demonstrated that decomposing PWV into linear and nonlinear components via wavelet transform can enhance prediction accuracy and among all the schemes tested, ARIMA-KOA-CNN-GRU-Attention performed the best, achieving a 52% RMSE reduction in comparison with the ARIMA-KOA-GRU method.