  With the recent increase in the number of underwater activities, having
effective underwater communication systems has become increasingly important.
Underwater acoustic communication has been widely used but greatly impaired due
to the complicated nature of the underwater environment. In a bid to better
understand the underwater acoustic channel so as to help in the design and
improvement of underwater communication systems, attempts have been made to
model the underwater acoustic channel using mathematical equations and
approximations under some assumptions. In this paper, we explore the capability
of machine learning and deep learning methods to learn and accurately model the
underwater acoustic channel using real underwater data collected from a water
tank with disturbance and from lake Tahoe. Specifically, Deep Neural Network
(DNN) and Long Short Term Memory (LSTM) are applied to model the underwater
acoustic channel. Experimental results show that these models are able to model
the underwater acoustic communication channel well and that deep learning
models, especially LSTM are better models in terms of mean absolute percentage
error.
