AiCareAir: Hybrid-Ensemble Internet-of-Things Sensing Unit Model for Air Pollutant Control

Jintu Borah, Md Shahrul Md Nadzir, Mylene G. Cayetano, Shubhankar Majumdar, Hemant Ghayvat, Gautam Srivastava*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The detrimental effects on human health caused by air pollution show that being able to predict air quality is a task of utmost significance. The application of artificial intelligence (AI) and the Internet of Things (IoT) is seen as promising in this domain. The performances of state-of-the-art models in terms of prediction accuracy vary with different pollutants and are acceptable only for certain pollutants. This article uses machine learning (ML) and deep learning (DL) models to predict the concentrations of six major air pollutants. Data are collected over eight months with 1400 daily instances from sensors deployed in Kuala Lumpur, Malaysia. As an intelligibly robust system, in this article a hybrid-ensemble model is proposed using a combination of ML models, specifically random forest, K-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and neural network (NN) models, namely, long short-term memory (LSTM), gated recurrent units (GRUs), and convolutional NNs (CNNs). Here, a hybrid-ensemble learning model is created using five various ML models as weak learners. In previous ensemble models, a homogeneous group of weak learners are used; however, this work uses a heterogeneous group of weak learners. The prediction accuracy is compared using R2 score, absolute, squared, and root-mean-squared errors (RMSEs).

Original languageEnglish
JournalIEEE Sensors Journal
Volume24
Issue number13
Pages (from-to)21558-21565
ISSN1530-437X
DOIs
Publication statusPublished - 2024

Keywords

  • Adam optimizer
  • convolutional neural networks (CNNs)
  • gated recurrent units (GRUs)
  • Keras API
  • long short-term memory (LSTM)
  • Scikit learn

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