  The spatial-temporal distribution of underwater sound velocity affects the
propagation mode of underwater acoustic signals. Therefore, rapid estimation
and prediction of underwater sound velocity distribution is crucial for
providing underwater positioning, navigation and timing (PNT) services.
Currently, sound speed profile (SSP) inversion methods have a faster time
response rate compared to direct measurement methods, however, most SSP
inversion methods focus on constructing spatial dimensional sound velocity
fields and are highly dependent on sonar observation data, thus high
requirements have been placed on observation data sources. To explore the
distribution pattern of sound velocity in the time dimension and achieve future
SSP prediction without sonar observation data, we propose a hierarchical long
short-term memory (H-LSTM) neural network for SSP prediction. By our SSP
prediction method, the sound speed distribution could be estimated without any
on-site data measurement process, so that the time efficiency could be greatly
improved. Through comparing with other state-of-the-art methods, H-LSTM has
better accuracy performance on prediction of monthly average sound velocity
distribution, which is less than 1 m/s in different depth layers.
