Smart City Gnosys
Smart city article details
| Title | A Deep Learning Approach For Prediction Of Air Quality Index In Smart City |
|---|---|
| ID_Doc | 1334 |
| Authors | Binbusayyis A.; Khan M.A.; Ahmed A M.M.; Emmanuel W.R.S. |
| Year | 2024 |
| Published | Discover Sustainability, 5, 1 |
| DOI | http://dx.doi.org/10.1007/s43621-024-00272-9 |
| Abstract | Industrial developments and consumption of massive amount of fossil fuels, vehicle pollution, and other calamities upsurges the AQI (Air Quality Index) of major cities in a drastic manner. Owing to these factors, it is important to take proactive measures for reducing the air pollution in order to avoid life- threatening consequence. Therefore, prediction of air quality is significant for improving the health of living beings as highly polluted regions have a higher concentration of pollutants mixed in the air, affecting the respiratory system and reducing the lifetime. To control pollution, AQI is used as a measure for estimating the pollutant content in the air. Even though many existing techniques have predicted AQI, enhancement is required in prediction algorithms with minimized loss. To address the challenges in traditional algorithms, the proposed smart cities-based AQI prediction intends to utilize the proposed regression algorithm in the dataset, namely Air- Quality-Data, which collected harmful pollutants on an hourly and daily basis from multiple cities in India between 2015 to 2020. To achieve prediction efficiency with reduced loss, pre-processing of input data is being performed using Deep GAN (Generative Adversarial Network). It performs the imputation of data in place of missing values to improve accurate prediction. Additionally, feature scaling normalizes independent real-data features to a fixed scale. With the processed data, regression is done through modified Stacked Attention GRU with KL divergence, which predicts Ernakulam, Chennai and Ahmedabad cities with higher, medium, and low levels of AQI in India. The performance of the proposed regression algorithm is measured using metrics such as MAE (Mean Absolute Error), MSE (Mean Square Error), R2 (Coefficient of determination), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error) and better MAE, MSE, R2, MAPE and RMSE obtained by the model is 0.1013, 0.0134, 0.9479, 0.1152 and 0.1156. Internal assessment and comparative analysis performed with existing regression algorithms exhibit lower loss values obtained from the present research, which determines the efficacy of the proposed model. © The Author(s) 2024. |
| Author Keywords | Ahmedabad; Air quality index; Chennai; Deep generative adversarial network; Ernakulam; Modified stacked attention GRU; Pollutants; Regression |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
1343  | 0.948 | 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) |
| 2472 | 0.923 | Mahalingam U.; Elangovan K.; Dobhal H.; Valliappa C.; Shrestha S.; Kedam G. | A Machine Learning Model For Air Quality Prediction For Smart Cities | 2019 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2019 (2019) |
| 42245 | 0.921 | 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) |
| 22959 | 0.921 | Vanitha M.; Narasimhan D. | Empowering Urban Planning With Accurate Air Quality Index Prediction: Hybrid Learning Models For Smart Cities | Deep Learning and Blockchain Technology for Smart and Sustainable Cities (2025) |
| 839 | 0.917 | Garg K.D.; Gupta M.; Sharma B.; Dhaou I.B. | A Comparison Of Regression Techniques For Prediction Of Air Quality In Smart Cities | 1st International Conference in Advanced Innovation on Smart City, ICAISC 2023 - Proceedings (2023) |
| 42717 | 0.914 | Mahajan A.; Mate S.; Vaidya H.; Kulkarni C.; Sawant S. | Predicting Lung Disease Severity Via Image-Based Aqi Analysis Using Deep Learning Techniques | 2024 Asian Conference on Intelligent Technologies, ACOIT 2024 (2024) |
| 15001 | 0.913 | 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) |
| 57441 | 0.91 | Pundir I.; Aggarwal N.; Singh S. | Time-Series Based Prediction Of Air Quality Index Using Various Machine Learning Models | Lecture Notes in Electrical Engineering, 1080 LNEE (2023) |
| 2458 | 0.908 | Swamynathan S.; Sneha N.; Ramesh S.P.; Niranjana R.; Ponkumar D.D.N.; Saravanakumar R. | A Machine Learning Approach For Predicting Air Quality Index In Smart Cities | Proceedings of 5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024 (2024) |
| 59852 | 0.908 | Dey S. | Urban Air Quality Index Forecasting Using Multivariate Convolutional Neural Network Based Customized Stacked Long Short-Term Memory Model | Process Safety and Environmental Protection, 191 (2024) |