| dc.description.abstract | Being able to accurately predict future levels of port congestion is of great value to both
port and ship operators. However, such a prediction tool is currently not available. In this
thesis, a Long Short-Term Memory Recurrent Neural Network is built to fulfill this need.
The prediction model uses information mined from Automatic Identification Systems (AIS)
data, vessel characteristics, weather data, and commodity price data as input variables
to predict the future level of congestion in the port of Paranaguå, Brazil. All data used
in this study are publicly available. The predictions of the proposed model are shown
to be promising with a satisfactory level of accuracy. The conclusion and evaluation of
the presented model are that it serves its purpose and fulfills its objective within the
constraints set by the authors and its inherent limitations. | en_US |
