Adaptive neuro fuzzy inference system for predicting sub-daily Zenith Wet Delay

Abstract

In recent years, the focus of tropospheric studies has evolved to GNSS meteorology and weather forecasting. The Zenith Wet Delay (ZWD), which might be assembled to the Integrated Water Vapour (IWV), is an essential component of the tropospheric delay. Acquiring predicted the ZWD with the required level of accuracy is crucial for weather forecasting. The scope of this study is to use the adaptive neural fuzzy inference system (ANFIS) to predict the ZWD for the following six-hour epoch based exclusively on the present the ZWD value. It was developed and verified using 505 geographically and internationally distributed stations which were used for training and testing from 2008 to 2019. It was assessed based on two criteria. First, the correlation coefficient (R) values were found to be more than 0.8 in 98% of the stations, including those with highest and lowest latitudes, and the remaining 2% of stations located in coastal areas. Second, the Root Mean Square Error (RMSE) values of the differences between the predicted and the actual ZWD were considered to be the more important finding of the study. That is, 99.21% of the 505 stations had the RMSE values equal to or less than 3 cm, with only 4 stations having the RMSE values higher (0.2 mm) than 3 cm. Since the results of this study achieved the required degree of accuracy from the predicted ZWD to be utilized in weather forecasting, they may also be beneficial for GNSS meteorology.