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

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 only. This paper uses Machine Learning (ML) and Deep Learning (DL) models to predict the concentrations of six major air pollutants. Data is collected over 8 months with 1400 daily instances from sensors deployed in Kuala Lumpur, Malaysia. As an intelligibly robust system, in this paper a hybrid ensemble model using a combination of ML models, specifically Random Forest, K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGBoost), and Neural Network models, namely Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), and Convolutional Neural Networks (CNN). 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 is utilized; 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.