Research Article
Features Inspired PM2.5 Prediction: A Belfast City Case Study
@INPROCEEDINGS{10.1007/978-3-031-67357-3_15, author={Fareena Naz and Muhammad Fahim and Adnan Ahmad Cheema and Nguyen Trung Viet and Tuan-Vu Cao and Trung Q. Duong}, title={Features Inspired PM2.5 Prediction: A Belfast City Case Study}, proceedings={Industrial Networks and Intelligent Systems. 10th EAI International Conference, INISCOM 2024, Da Nang, Vietnam, February 20--21, 2024, Proceedings}, proceedings_a={INISCOM}, year={2024}, month={7}, keywords={Feature generation Signal decomposition PM2.5 Machine learning Forecasting models Long Short Term Memory}, doi={10.1007/978-3-031-67357-3_15} }- Fareena Naz
Muhammad Fahim
Adnan Ahmad Cheema
Nguyen Trung Viet
Tuan-Vu Cao
Trung Q. Duong
Year: 2024
Features Inspired PM2.5 Prediction: A Belfast City Case Study
INISCOM
Springer
DOI: 10.1007/978-3-031-67357-3_15
Abstract
Air pollution is one of the key challenges to both human health and our environment, and managing it requires collective systematic efforts to prevent and mitigate future effects. Fundamentally, this required a better understanding of sources that generate pollution and forecasting models to predict current and future air pollution levels. In this work, we investigated features inspired PM2.5 prediction based on a dataset collected in Northern Ireland, UK. We analysed the influence of different features available in the dataset and newly generated with approaches such as Variational Mode Decomposition (VMD) and evaluated single-step forecasting model performance. We found that a single Long Short Term Memory (LSTM) layer model with a small number of cells and integrated features are sufficient to achieve a good forecasting performance. The combination of VMD integrated features enabled the forecasting model to achieve(\text {R}^2)score over 85% and achieve a gain of 6% when compared with lag based prediction only.
