  Maritime traffic emissions are a major concern to governments as they heavily
impact the Air Quality in coastal cities. Ships use the Automatic
Identification System (AIS) to continuously report position and speed among
other features, and therefore this data is suitable to be used to estimate
emissions, if it is combined with engine data. However, important ship features
are often inaccurate or missing. State-of-the-art complex systems, like CALIOPE
at the Barcelona Supercomputing Center, are used to model Air Quality. These
systems can benefit from AIS based emission models as they are very precise in
positioning the pollution. Unfortunately, these models are sensitive to missing
or corrupted data, and therefore they need data curation techniques to
significantly improve the estimation accuracy. In this work, we propose a
methodology for treating ship data using Conditional Restricted Boltzmann
Machines (CRBMs) plus machine learning methods to improve the quality of data
passed to emission models. Results show that we can improve the default methods
proposed to cover missing data. In our results, we observed that using our
method the models boosted their accuracy to detect otherwise undetectable
emissions. In particular, we used a real data-set of AIS data, provided by the
Spanish Port Authority, to estimate that thanks to our method, the model was
able to detect 45% of additional emissions, of additional emissions,
representing 152 tonnes of pollutants per week in Barcelona and propose new
features that may enhance emission modeling.
