GNSS-IR real-time water level retrieval method based on hybrid sliding window and LSTM

Real-time water level monitoring is of critical significance in flood disaster mitigation and water resource management. This paper proposes a real-time Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) water level retrieval method based on the hybrid integration of sliding window and Long Short-Term Memory (LSTM). By dynamically updating input sequences through the sliding window mechanism, an LSTM model captures both temporal and nonlinear characteristics of water level variations, enabling high-precision real-time prediction. Experimental results demonstrate that during non-typhoon seasons, the predicted sea level achieves a correlation coefficient of 99.78% and a root mean square error (RMSE) of 10.81 cm compared to tide gauge measurements. The system still formulates stable predictions for near-real-time sea level monitoring even with 1.31% data gaps caused by missing values, which satisfies the requirements. During storm surge, the correlation coefficient between predicted and measured data reaches 96.18%, with a RMSE of 16.55 cm. Notably, the method maintains robust real-time predictive capability even under extreme conditions where wind speeds exceed 30 m/s and retrieval values significantly decrease. These results demonstrate that the proposed method achieves high accuracy under both normal and extreme hydrological conditions, providing an efficient, cost-effective technical pathway for nearshore real-time water level monitoring and disaster early warning. • Integrate a sliding window mechanism with LSTM to dynamically capture temporal dependencies and nonlinear dynamics in GNSS-IR water level retrieval. • Train the LSTM model with water level observations of different temporal scales to establish a predictive foundation linking long-term learning with near-real-time monitoring. • Ensure robust real-time prediction performance under data gaps and extreme hydrological conditions, supporting disaster early warning applications.