Abstract :

Sea level rise poses increasing risks to coastal regions, highlighting the need for accurate and reliable forecasting methods. This study proposes a probabilistic sea level forecasting framework by integrating a Long Short-Term Memory (LSTM) model with the Moving Block Bootstrap (MBB) technique. The LSTM model is used to capture nonlinear temporal dependencies in sea level time-series data, while the bootstrap approach is employed to quantify prediction uncertainty through probabilistic forecasting. The LSTM model achieved high deterministic prediction accuracy with an MSE of 2.11 × 10^!”, RMSE of 0.00459, MAE of 0.00356, and MAPE of 0.34%. The proposed hybrid MBB–LSTM model generates probabilistic forecasts with a 95% confidence interval, resulting in an MSE of 0.01155, RMSE of 0.10749, MAE of 0.08370, and MAPE of 8.99%. Forecast results indicate relatively stable sea level variability until 2026 with an estimated rising trend of approximately 7.44 mm per year. The proposed hybrid framework provides a more informative prediction approach by combining deep learning with bootstrap-based uncertainty estimation, which is valuable for coastal risk assessment and climate adaptation planning.

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Keywords :

Long Short-Term Memory, Moving Block Bootstrap, Probabilistic forecasting, Sea level prediction, Sea level rise., Time series analysis

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