SDIPPWV: A novel hybrid prediction model based on stepwise decomposition-integration-prediction avoids future information leakage to predict precipitable water vapor from GNSS observations

Fanming Wu; Dengao Li; Jumin Zhao; Hairong Jiang; Xinyu Luo

doi:10.1016/j.scitotenv.2024.173116

SDIPPWV: A novel hybrid prediction model based on stepwise decomposition-integration-prediction avoids future information leakage to predict precipitable water vapor from GNSS observations

Sci Total Environ. 2024 May 9:933:173116. doi: 10.1016/j.scitotenv.2024.173116. Online ahead of print.

Authors

Fanming Wu¹, Dengao Li², Jumin Zhao³, Hairong Jiang⁴, Xinyu Luo⁴

Affiliations

¹ College of Computer Science and Technology, College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China.
² College of Computer Science and Technology, College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China; Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan 030024, China. Electronic address: lidengao@tyut.edu.cn.
³ College of Electronic Information and Optical Engineering, Taiyuan 030024, China; Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan 030024, China.
⁴ College of Electronic Information and Optical Engineering, Taiyuan 030024, China.

PMID: 38734080
DOI: 10.1016/j.scitotenv.2024.173116

Abstract

Water vapor is an important meteorological parameter. Accurate prediction of water vapor content can be used to provide important reference information for heavy rainfall forecast and artificial precipitation operation. The current water vapor hybrid prediction model has the problem of future data leakage, and the error is accumulated by reconstructing the subsequence after prediction. Therefore, this paper proposes a stepwise decomposition-integration-prediction precipitable water vapor mechanism, named SDIPPWV, which can effectively solve the above problems. Firstly, High-precision precipitable water vapor (PWV) sequence is retrieved from Global Navigation Satellite System (GNSS) observation files. Then stepwise decomposition process uses a fixed-size window to segment the PWV sequence and Seasonal-Trend decomposition based on Loess (STL) to decompose the sequences within the window. Next, the features of the three sub-sequences are integrated to construct the feature space. Finally the prediction of PWV is obtained using 1D Convolutional Neural Network-Bidirectional Long Short Term Memory (1D CNN-BiLSTM). The model performance is verified using observation data from eight GNSS stations. The performance of the PWV prediction model proposed in this paper is effectively improved compared with the single prediction models and other hybrid models. The mean root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R²) of the eight stations are 0.2146 mm, 0.1132 mm, 1.29 %, and 0.9998, respectively. The results show that the model proposed in this paper improves the prediction accuracy of water vapor content while solving the data leakage problem.

Keywords: 1D CNN-BiLSTM; GNSS PWV; Seasonal-Trend decomposition based on Loess; Stepwise decomposition-integration-prediction; Water vapor prediction.