Smart City Gnosys
Smart city article details
| Title | Timezone-Aware Auto-Regressive Long Short-Term Memory Model For Multipollutant Prediction |
|---|---|
| ID_Doc | 57455 |
| Authors | Borah J.; Nadzir M.S.M.; Cayetano M.G.; Ghayvat H.; Majumdar S.; Srivastava G. |
| Year | 2025 |
| Published | IEEE Transactions on Systems, Man, and Cybernetics: Systems, 55, 1 |
| DOI | http://dx.doi.org/10.1109/TSMC.2024.3463960 |
| Abstract | Air pollution poses a significant threat to urban environments, and accurate prediction of multiple air pollutants is crucial for effective mitigation strategies. This study introduces a novel time-aware auto-regressive long-short-term memory (TAR LSTM) approach to address this challenge by developing a multivariate prediction model using artificial intelligence (AI) for SMART city applications. Existing models often fall short of predicting all six major criteria pollutants comprehensively. In response, this work proposes an autoregressive (AR) neural network model based on the long short-term memory (LSTM) architecture, which excels in capturing temporal dependencies within sequential data. The proposed method uses the AR model that captures the linear dependencies in the time series, while the LSTM captures the nonlinear dependencies and long-term patterns. This enables the model to consider past pollutant concentrations and their relationships, resulting in a more accurate and dynamic prediction. Rigorous testing on datasets from low-cost air quality sensors (LAQSs) validates the model's superior performance. Datasets from diverse locations, including India, Malaysia, and the Philippines, contribute to the robustness of the model, showcasing its efficacy in varied urban environments. This research contributes to advancing predictive modeling for air quality, addressing the limitations of previous approaches, and providing a promising solution for SMART city implementations. The findings highlight the AR LSTM model's potential as a valuable tool for precise and comprehensive air pollution forecasting, which has implications for informed decision making and better urban environmental management. © 2013 IEEE. |
| Author Keywords | Air pollution prediction; artificial intelligence (AI); auto-regression; long short-term memory (LSTM); neural network; time series |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
59852  | 0.921 | 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) |
| 40562 | 0.917 | Dalal S.; Lilhore U.K.; Faujdar N.; Samiya S.; Jaglan V.; Alroobaea R.; Shaheen M.; Ahmad F. | Optimising Air Quality Prediction In Smart Cities With Hybrid Particle Swarm Optimization-Long-Short Term Memory-Recurrent Neural Network Model | IET Smart Cities, 6, 3 (2024) |
| 42833 | 0.91 | Narayan A.; Das A.K. | Prediction Of Pm2.5 In Smart City Using Modified Lstm With Connected Memory | Lecture Notes in Networks and Systems, 1266 LNNS (2025) |
| 50861 | 0.904 | Faydi M.; Zrelli A.; Ezzedine T. | Smart Environment Monitoring Systems For Pm2.5 Prediction Using Deep Learning Models In Smart City | 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023 (2023) |
| 22959 | 0.9 | 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) |
| 26309 | 0.895 | Chatterjee K.; Gurujyothi S.; Kumar B.K.; Selvamuthukumaran N.; Suraj S.; Reddy M.S.; Sachin A.; Thara M.N. | Fed-Rest: Federated Learning-Based Recurrent Long Short-Term Memory Model For Smart Cities Air Quality Prediction | Lecture Notes in Networks and Systems, 1045 LNNS (2024) |
| 37690 | 0.892 | Pereira L.; Tamilselvi P. | Modelling Of Fusion Artificial Neural Networks For Assessment Of Air Pollution In Smart City Environment | Proceedings of International Conference on Circuit Power and Computing Technologies, ICCPCT 2024 (2024) |
| 7132 | 0.892 | Jovova L.; Trivodaliev K. | Air Pollution Forecasting Using Cnn-Lstm Deep Learning Model | 2021 44th International Convention on Information, Communication and Electronic Technology, MIPRO 2021 - Proceedings (2021) |
| 2182 | 0.891 | Huang B.-X.; Lee S.-J. | A Hybrid Long Short-Term Memory Network Based On Wind And Rain Sensitive And Its Application To Pm2.5 Prediction | Lecture Notes in Networks and Systems, 766 (2023) |
| 60731 | 0.891 | Samal K.K.R. | Utilizing Deep Learning Techniques For A Multi-Objective Pollution Forecasting Model To Enhance Smart City Sustainability | Intelligent Computing and Emerging Communication Technologies, ICEC 2024 (2024) |