A machine learning approach to achieve accurate time series forecast of sea-wave conditions

Reliable forecast of environmental variables is fundamental in managing
risk associated with hazard scenarios. In this work, we use state of the
art machine learning algorithms to build forecasting models and to get
accurate estimation of sea wave conditions. We exploit multivariate time
series of environmental variables, extracted either from hindcast
database (provided by MeteOcean Group at DICCA) or observed data from
sparse buoys. In this way, future values of sea wave height can be
predicted in order to evaluate the risk associated with incoming
scenarios. The aim is to provide new forecasting tools representing an
alternative to physically based models which have higher computational
cost.