The WOA–CNN–LSTM–Attention Model for Predicting GNSS Water Vapor

Abstract

Precipitable water vapor (PWV), as an important representative parameter of atmospheric water vapor contents, can be obtained by means of Global Navigation Satellite Systems (GNSS) using both ground-based and space-borne observation techniques. However, the PWV prediction models currently accessible tend to be simplistic combinations or individual models. In this study, we develop a whale optimization algorithm (WOA) convolutional neural network (CNN) long short-term memory (LSTM)-Attention model to predict PWV, which takes the 16 GNSS PWV values near the King’s Park (HKKP) station as characteristic parameters and the spatial relationship between the point of interest and its neighboring GNSS stations into consideration. An optimal model via the WOA is investigated by using a wavelet analysis to separate noises, through combining CNN, LSTM neural network, and attention mechanism. Results show that considerable improvement in the prediction accuracy has been achieved through a comparison between CNN-LSTM–Attention and the conventional LSTM and CNN-LSTM models. In terms of long-term predictability, CNN-LSTM–Attention is proven to be a superior model when eight features are incorporated. The model’s root mean square error (RMSE) is 2.30 mm, which is reduced by 20.42% than in the case of 0 feature is used. As a further analysis, we also examine the prediction performance of various models for hourly PWV using 7, 15, 30, 60, and 90 days of data as different lengths of training. The results show that CNN-LSTM–Attention has a better prediction effect when the training length is 30 days, the RMSE is 0.74 mm, and the Nash-Sutcliffe efficiency (NSE) coefficient is 0.98.