Abstract:
Within the BlueSafePort project an Early Warning System
(EWS) is being developed for forecasting and alerting
emergency situations related to ship navigation in ports,
as well as operational constraints. Port terminals
downtime leads to large economic losses and largely
affects the port’s overall competitiveness. So, the goal of
such EWS is to reduce the port’s vulnerability by
increasing its planning capacity and efficient response to
emergency situations. As any EWS, its usefulness
depends greatly on its reliability and accuracy. To achieve
more accurate predictions a new method was developed
to optimize forecasts produced by the system. Using
available database from buoys, pressure sensors and
meteorological stations, neural networks were trained to
optimize numerical models results.
