Deep collaborative learning model for port-air pollutants prediction using automatic identification system

Abstract

Air pollution in port cities is aggravated by ship pollutant emissions. A deep collaborative learning (DCL)-based prediction model using automatic identification system (AIS) is proposed in this study to predict this type of pollution. In the model training process, a novel data pre-processing method was devised to efficiently handle heterogeneous data: air pollution, weather conditions, and AIS data. To combine these data together, a pretraining step is introduced using an autoencoder-based model, which is a customized convolutional long short-term memory network model, followed by a DCL method for the prediction of highly accurate air pollution values (for both short-and long-term predictions). Compared with other approaches, this method showed on an average, a performance improvement of nearly 10% in terms of the root mean squared error. Experiments to test and validate the model were conducted near the North/Old Busan Port, Republic of Korea.