Smart City Gnosys

Smart city article details

Title	Ai- And Iot-Based Hybrid Model For Air Quality Prediction In A Smart City With Network Assistance
ID_Doc	6972
Authors	Kataria A.; Puri V.
Year	2022
Published	IET Networks, 11, 6
DOI	http://dx.doi.org/10.1049/ntw2.12053
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 PM2.5 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 (R2), 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. © 2022 The Authors. IET Networks published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
Author Keywords	5G Communication; AQI; Bayesian Optimization Algorithm; CNN; IoT; Kalman filter; LSTM

Similar Articles

Id	Similarity	Authors	Title	Published
33702	0.918	Alnowaiser K.; Alarfaj A.A.; Alabdulqader E.A.; Umer M.; Cascone L.; Alankar B.	Iot Based Smart Framework To Predict Air Quality In Congested Traffic Areas Using Sv-Cnn Ensemble And Knn Imputation Model	Computers and Electrical Engineering, 118 (2024)
15001	0.916	Sharma G.; Khurana S.; Saina N.; Shivansh; Gupta G.	Comparative Analysis Of Machine Learning Techniques In Air Quality Index (Aqi) Prediction In Smart Cities	International Journal of System Assurance Engineering and Management, 15, 7 (2024)
1343	0.916	Ghose B.; Rehena Z.; Anthopoulos L.	A Deep Learning Based Air Quality Prediction Technique Using Influencing Pollutants Of Neighboring Locations In Smart City	Journal of Universal Computer Science, 28, 8 (2022)
7559	0.916	Samee I.U.; Jilani M.T.; Wahab H.G.A.	An Application Of Iot And Machine Learning To Air Pollution Monitoring In Smart Cities	2019 4th International Conference on Emerging Trends in Engineering, Sciences and Technology, ICEEST 2019 (2019)
27055	0.914	Varade H.P.; Bhangale S.C.; Thorat S.R.; Khatkale P.B.; Sharma S.K.; William P.	Framework Of Air Pollution Assessment In Smart Cities Using Iot With Machine Learning Approach	Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023 (2023)
7141	0.913	Catovic A.; Kadusic E.; Ruland C.; Zivic N.; Hadzajlic N.	Air Pollution Prediction And Warning System Using Iot And Machine Learning	International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022 (2022)
42245	0.911	Faydi M.; Zrelli A.; Ezzedine T.	Pm2.5 Prediction Using Deep Learning Models	17th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2023 - Proceedings (2023)
31860	0.91	Samal A.; Samal L.; Swain A.K.; Mahapatra K.	Integrated Iot-Based Air Quality Monitoring And Prediction System: A Hybrid Approach	Proceedings - 2023 IEEE International Symposium on Smart Electronic Systems, iSES 2023 (2023)
35552	0.91	Lavanya P.; Reddy I.V.S.; Selvakumar V.	Long Range Radio Technology Implementation On Internet Of Things To Detect Particulate Matter At The Community Level And Prediction Using Machine Learning Based Approach	Engineered Science, 29 (2024)
7160	0.906	Sonawani S.; Patil K.	Air Quality Measurement, Prediction And Warning Using Transfer Learning Based Iot System For Ambient Assisted Living	International Journal of Pervasive Computing and Communications, 20, 1 (2024)