Sea level forecasting is useful for many coastal
applications, such as navigation, coastal operation, etc. One of
many challenges in sea level forecasting is the availability of
its historical data. Limited amount of historical data may lead
to inaccurate prediction. Especially when using tidal harmonic
analysis, low frequency components cannot be captured in short
term historical data. In this paper, we present an application of a
deep learning approach for sea level forecasting, especially with
limited amount of historical sea level data. We use the so-called
Long Short Term Memory (LSTM) method to forecast the sea
level. We use two months of historical data, to forecast 7 days up
to two months ahead. As a study case, we use the sea level data
that is recorded in Tanjung Benoa, Bali, Indonesia. To investigate
its accuracy, we compare results of forecasting by using the
LSTM with the traditional method tidal harmonic analysis that
uses Least Square Estimation (LSE) method. Moreover, we also
compare the LSTM method with feedback and no-feedback. The
results of forecasting by using LSTM with feedback results in
accurate prediction, with R value of 0.99.
Index Terms—Sea Level, Limited Historical Data, Long Short
Term Memory Networks (LSTM)