Improving maritime traffic emission estimations on missing data with CRBMs. (September 2020)

• Record Type: Journal Article
• Authors: Gutierrez-Torre, Alberto; Berral, Josep Ll.; Buchaca, David; Guevara, Marc; Soret, Albert; Carrera, David
• Is Part Of: Engineering applications of artificial intelligence. Volume 94(2020)
• Publication Date: 2020-09
• Subjects: Data cleaning -- AIS -- Emission modeling -- CRBM -- Ship time series -- GPS; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103793
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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. Highlights: Generate feature representations of local behavior of ship movements. Ship attribute correction with the new local behavior features. Improvement of emission estimation using ship attribute corrections. Provide an initial step to correct and improve Navigational Status AIS features.