2026-04-05T12:50:08Zhttps://recercat.cat/oai/request
oai:recercat.cat:2117/3291942026-02-07T06:44:49Zcom_2072_1033col_2072_452950
RECERCAT author Gutiérrez Torre, Alberto author Berral García, Josep Lluís author Buchaca Prats, David author Guevara Vilardell, Marc author Soret, Albert author Carrera Pérez, David 2020-07-07 © Elsevier 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/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 that can also be applied to other GPS and time-series problems. 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, representing 152 tonnes of pollutants per week in Barcelona and propose new features that may enhance emission modeling.This project hasreceived funding from the European Research Council (ERC) under the European Union Horizon 2020 research and innovation programme (grant agreement No 639595). This work is partially supported by the Ministry of Economy, Industry and Competitiveness of Spain under contracts TIN2015-65316-P,2014SGR1051, IJCI2016-27485, and Severo Ochoa Center of Excellence SEV-2015-0493-16-5.Peer ReviewedPostprint (published version) https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access Attribution-NonCommercial-NoDerivatives 4.0 International Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Àrees temàtiques de la UPC::Nàutica::Impacte ambiental Artificial intelligence Navigation -- Environmental aspects Data cleaning AIS Emission modeling CRBM Ship time series GPS Intel·ligència artificial Navegació -- Aspectes ambientals Improving maritime traffic emission estimations on missing data with CRBMs Article