Forecasting of Sea Level Time Series using RNN and LSTM Case Study in Sunda Strait

Abstract

Sea-level forecasting is essential for coastal development planning and minimizing their signi?cant consequences in coastal operations, such as naval engineering and navigation. Conventional sea level predictions, such as tidal harmonic analysis, do not consider the in?uence of non-tidal elements and require long-term historical sea level data. In this paper, two deep learning approaches are applied to forecast sea level. The ?rst deep learning is Recurrent Neural Network (RNN), and the second is Long Short Term Memory (LSTM). Sea level data was obtained from IDSL (Inexpensive Device for Sea Level Measurement) at Sebesi, Sunda Strait, Indonesia. We trained the model for forecasting 3, 5, 7, 10, and 14 days using three months of hourly data in 2020 from 1st May to 1st August. We compared forecasting results with RNN and LSTM with the results of the conventional method, namely tidal harmonic analysis. The LSTM’s results showed better performance than the RNN and the tidal harmonic analysis, with a correlation coef?cient of R2 0.97 and an RMSE value of 0.036 for the 14 days prediction. Moreover, RNN and LSTM can accommodate non-tidal harmonic data such as sea level anomalies.