Improving Maritime Traffic Emission Estimations on Missing Data with CRBMs

TL;DR

This paper introduces a novel approach using Conditional Restricted Boltzmann Machines (CRBMs) to improve the accuracy of maritime emission estimations by effectively handling missing or corrupted AIS data, thereby enhancing air quality modeling.

Contribution

The paper presents a new methodology combining CRBMs and machine learning to enhance AIS data quality for emission estimation, outperforming existing data imputation techniques.

Findings

Improved detection of emissions by 45% using the proposed method.

Estimated additional emissions of 152 tonnes of pollutants weekly in Barcelona.

Enhanced features for better emission modeling accuracy.

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. 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.