A Refined Prediction Model for Regional Zenith Troposphere Combining ICEEMDAN and BiLSTM-XGBoost.
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| Title: | A Refined Prediction Model for Regional Zenith Troposphere Combining ICEEMDAN and BiLSTM-XGBoost. |
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| Authors: | Chen, Chao1 (AUTHOR) cchen_chn@aust.edu.cn, Zhao, Yinghao1,2 (AUTHOR), Zhang, Wenyuan2,3 (AUTHOR), Ge, Yulong1,3 (AUTHOR), Yuan, Jiajia1,2 (AUTHOR), Hu, Chao1,3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1381. 17p. |
| Subjects: | Long short-term memory, Boosting algorithms, Satellite meteorology, Time series analysis |
| Geographic Terms: | China |
| Abstract: | Highlights: What are the main findings? An ICEEMDAN-based reconstruction strategy was used to separate ZTD variations into high-, medium-, and low-frequency components, which improved the representation of non-stationary atmospheric signals. The proposed ICEEMDAN-BiLSTM-XGBoost (IBX) model achieved the best overall performance among the tested models, with a mean root mean square error (RMS) of 14.17 mm and a mean absolute error (MAE) of 10.24 mm over the 1–12 h forecasting horizons across 27 global navigation satellite system (GNSS) stations in China. What are the implications of the main findings? The RMS-based fusion of BiLSTM and XGBoost reduced error growth in multi-step rolling prediction and improved forecasting stability under different station environments. The proposed framework provides a physically interpretable and statistically robust approach for short- to medium-term ZTD forecasting, with potential applications in GNSS meteorology and real-time atmospheric delay correction. To address the degradation of zenith tropospheric delay (ZTD) prediction accuracy caused by time-varying noise and error accumulation in multi-step forecasting, this study proposes an integrated prediction model, named IBX, which combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bidirectional long short-term memory (BiLSTM), and extreme gradient boosting (XGBoost). In the proposed framework, ICEEMDAN is first used to decompose the original ZTD series into components at different temporal scales. A three-criterion reconstruction strategy based on the Pearson correlation coefficient, dominant period, and sample entropy is then applied to obtain high-, medium-, and low-frequency subsequences with clearer physical meanings. BiLSTM and XGBoost are used to predict the reconstructed components, and their outputs are fused through a root mean square error (RMS)-based weighting strategy to improve forecasting robustness. Hourly ZTD data from 27 global navigation satellite system (GNSS) stations in China from 2011 to 2020 were used for model validation under 1–12 h rolling forecasting horizons. The results show that IBX achieves the best overall performance among the tested models. Its mean RMS and mean absolute error (MAE) over the 1–12 h horizons are 14.17 mm and 10.24 mm, respectively, which are 22.5% and 21.4% lower than those of the baseline BiLSTM model. Spatial and climate-region-based analyses further indicate that ZTD prediction accuracy is strongly affected by altitude, regional moisture conditions, and climate type. The proposed IBX model shows stable error suppression across heterogeneous station environments, especially in the temperate monsoon region and low-altitude regions with complex water vapor variability. These results demonstrate that IBX provides a reliable and physically interpretable approach for short- to medium-term ZTD forecasting and real-time atmospheric delay correction. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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