Prediction of air quality in Jakarta during the COVID-19 outbreak using long short-term memory machine learning

Abstract

Air pollution is one of the world’s problems, not just one location. This air pollution is caused by pollutants that are harmful to human health and the environment. Some pollutants are most influential, namely particulate matter, ground-level ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide. Several countries decided to lock down when the COVID-19 outbreak was announced simultaneously throughout the world like a pandemic. In Jakarta, Indonesia applies large-scale social restrictions (PSBB). The resulting impact is a drastic reduction in air pollution on air quality. This paper aims to predict air quality during the COVID-19 outbreak in Jakarta using long short-term memory (LSTM) machine learning. The evaluation of the LSTM model used in this paper is the root mean square error (RMSE). The results obtained show that the Adam optimizer can bring the prediction results closer to the dataset used.

Air Pollution in Jakarta, Indonesia Under Spotlight: An AI-Assisted Semi-Supervised Learning Approach

“…They believed that the large-scale social restrictions (PSBB) imposed during the epidemic resulted in a significant reduction in Jakarta's air pollution. The root mean square error (RMSE) is utilized to evaluate the LSTM model in this study, and the results suggest that the Adam optimizer can bring the prediction results closer to the dataset used [15]. In addition, Marviola Hardini and colleagues proposed the use of convolutional neural networks (CNN) and image-based machine learning to estimate air quality.…”