  The hydrodynamic performance of a sea-going ship varies over its lifespan due
to factors like marine fouling and the condition of the anti-fouling paint
system. In order to accurately estimate the power demand and fuel consumption
for a planned voyage, it is important to assess the hydrodynamic performance of
the ship. The current work uses machine-learning (ML) methods to estimate the
hydrodynamic performance of a ship using the onboard recorded in-service data.
Three ML methods, NL-PCR, NL-PLSR and probabilistic ANN, are calibrated using
the data from two sister ships. The calibrated models are used to extract the
varying trend in ship's hydrodynamic performance over time and predict the
change in performance through several propeller and hull cleaning events. The
predicted change in performance is compared with the corresponding values
estimated using the fouling friction coefficient ($\Delta C_F$). The ML methods
are found to be performing well while modelling the hydrodynamic state
variables of the ships with probabilistic ANN model performing the best, but
the results from NL-PCR and NL-PLSR are not far behind, indicating that it may
be possible to use simple methods to solve such problems with the help of
domain knowledge.
