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oai:recercat.cat:2117/3291942026-02-07T06:44:49Zcom_2072_1033col_2072_452950

Improving maritime traffic emission estimations on missing data with CRBMs Gutiérrez Torre, Alberto Berral García, Josep Lluís Buchaca Prats, David Guevara Vilardell, Marc Soret, Albert Carrera Pérez, David Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors Barcelona Supercomputing Center Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions 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 © 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 Reviewed Postprint (published version) 2020-07-07 Article Gutiérrez-Torre, A. [et al.]. Improving maritime traffic emission estimations on missing data with CRBMs. "Engineering applications of artificial intelligence", 7 Juliol 2020, vol. 94, p. 103793:1-103793:10. 0952-1976 https://arxiv.org/pdf/2009.03001.pdf https://hdl.handle.net/2117/329194 10.1016/j.engappai.2020.103793 eng https://www.sciencedirect.com/science/article/abs/pii/S0952197620301822?via%3Dihub info:eu-repo/grantAgreement/EC/H2020/639595/EU/Holistic Integration of Emerging Supercomputing Technologies/Hi-EST info:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/ info:eu-repo/grantAgreement/AGAUR/PRI2010-2013/2014 SGR 1051 info:eu-repo/grantAgreement/MINECO//SEV-2015-0493/ES/BARCELONA SUPERCOMPUTING CENTER - CENTRO. NACIONAL DE SUPERCOMPUTACION/ https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access Attribution-NonCommercial-NoDerivatives 4.0 International application/pdf Elsevier