Applying and Comparing LSTM and ARIMA to Predict CO Levels for a Time-Series Measurements in a Port Area

Abstract

Air pollution is a major problem in the everyday life of citizens, especially air pollution in the transport domain. Ships play a significant role in coastal air pollution, in conjunction with transport mobility in the broader area of ports. As such, ports should be monitored in order to assess air pollution levels and act accordingly. In this paper, we obtain CO values from environmental sensors that were installed in the broader area of the port of Igoumenitsa in Greece. Initially, we analysed the CO values and we have identified some extreme values in the dataset that showed a potential event. Thereafter, we separated the dataset into 6-h intervals and showed that we have an extremely high rise in certain hours. We transformed the dataset to a moving average dataset, with the objective being the reduction of the extremely high values. We utilised a machine-learning algorithm, namely the univariate long short-term memory (LSTM) algorithm to provide the predicted outcome of the time series from the port that has been collected. We performed experiments by using 100, 1000, and 7000 batches of data. We provided results on the model loss and the root-mean-square error as well as the mean absolute error. We showed that with the case with batch number equals to 7000, the LSTM we achieved a good prediction outcome. The proposed method was compared with the ARIMA model and the comparison results prove the merit of the approach.