Features Inspired PM2.5 Prediction: A Belfast City Case Study

Fareena Naz, Muhammad Fahim, Adnan Ahmad Cheema, Nguyen Trung Viet, Tuan-Vu Cao, Trung Q. Duong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 data set 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 R2 score over 85% and achieve a gain of 6% when compared with lag based prediction only.
Original languageEnglish
Title of host publication10th EAI International Conference on Industrial Networks and Intelligent Systems: Proceedings
Pages1-10
Number of pages10
Publication statusAccepted/In press - 28 Dec 2023
Event10th EAI International Conference on Industrial Networks and Intelligent Systems: Proceedings - Viet Nam, Da Nang, Viet Nam
Duration: 20 Feb 202421 Feb 2024
https://iniscom.eai-conferences.org/2024/

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X

Conference

Conference10th EAI International Conference on Industrial Networks and Intelligent Systems: Proceedings
Abbreviated titleEAI INISCOM 2024
Country/TerritoryViet Nam
CityDa Nang
Period20/02/2421/02/24
Internet address

Keywords

  • Feature generation
  • Signal decomposition
  • PM2.5
  • machine learning
  • Forecasting models
  • Long short term memory approach
  • Air pollution prediction
  • Health
  • Air quality

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