AI‐ and IoT‐based hybrid model for air quality prediction in a smart city with network assistance. Issue 6 (20th August 2022)

• Record Type: Journal Article
• Authors: Kataria, Aman; Puri, Vikram
• Is Part Of: IET networks. Volume 11:Issue 6(2022)
• Page Start: 221
• Page End: 233
• Publication Date: 2022-08-20
• Subjects: 5G Communication -- AQI -- Bayesian Optimization Algorithm -- CNN -- IoT -- Kalman filter -- LSTM; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/ntw2.12053
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract: Air pollution is one of the biggest concerns in the world but it has not been paid much attention in developing countries. It is necessary to design models and methods to understand air pollution in developing countries to reduce the rate of pollution. This paper proposes an Internet of Things (IoT) and Artificial Intelligence (AI)‐based hybrid model to predict the Air Quality Index (AQI) with a practical case study of the public data sets. The sensor node is deployed in the city to collect air quality data. Moreover, this sensor node connects to the cloud server for collecting data at the firebase real‐time database through a WiFi/5G network embedded in the raspberry controller. Carbon monoxide (CO) and fine particular matter PM 2.5 sensors are integrated within a sensor node to monitor the AQI of the regions. A Kalman fis also applied to remove unwanted noise from the data collected through the sensor node. Models namely Artificial Neural Network (ANN), Support Vector Machine (SVM), k‐nearest neighbour (k‐NN), Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), CNN‐LSTM, ensemble model, and a proposed model, that is, CNN‐LSTM‐Bayesian optimization algorithm (BOA) model, have been utilised to predict the AQI. The performance evaluation of models is done through statistical parameters, such as mean absolute error (MAE), root mean square error (RMSE), coefficient of determination ( R 2 ), and accuracy score on two different public data sets and compared with the baseline models. The performance of the CNN‐LSTM‐BOA model is better than baseline models in terms of above‐mentioned statistical parameters as the accuracy reported is more than 97 %.This study can help predict the Air Quality Index and provide sufficient time to generate warning signals in the location.