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.
翻译:船舶使用自动识别系统(AIS)不断报告位置和速度等特性,因此,如果数据与引擎数据相结合,这些数据就适宜用于估算排放量。然而,重要的船舶特征往往不准确或缺失。像巴塞罗那超光速计算中心的CALIOPE这样的最先进的复杂系统被用于模拟空气质量。这些系统可以从基于AIS的排放模型中受益,因为它们在定位污染方面非常精确。不幸的是,这些模型对丢失或腐败的数据十分敏感,因此它们需要数据整理技术来大幅提高估算准确性。在这项工作中,我们提出了一种方法,用以使用Contatial Restricted Boltzmann机器(CRBMS)和机器学习方法处理船舶数据,以提高排放模型数据的质量。结果显示,我们可以改进拟议的默认模型方法,以覆盖缺失的数据。我们发现,使用我们的方法提高了模型的准确性,以检测无法检测出无法检测的不检测的准确性数据,因此,它们需要使用数据调节技术来大幅提高估算准确性。 在这项工作中,我们利用了一种真正的数据方法,我们用一种能够测量排放量的方法, 来测量,从而测量我们的标准。