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Smart city article details
| Title | Empowering Urban Planning With Accurate Air Quality Index Prediction: Hybrid Learning Models For Smart Cities |
|---|---|
| ID_Doc | 22959 |
| Authors | Vanitha M.; Narasimhan D. |
| Year | 2025 |
| Published | Deep Learning and Blockchain Technology for Smart and Sustainable Cities |
| DOI | http://dx.doi.org/10.1201/9781003476047-13 |
| Abstract | Using the hybrid deep learning (DL) models, this paper offers a unique method for forecasting the Air Quality Index (AQI) in cities that are sustainable and smart with the goal of improving accuracy while cutting down on computing time. Since traditional AQI prediction algorithms have limited accuracy and considerable computing costs, they often fail to satisfy the changing needs of urban environments. We suggest a hybrid DL framework that integrates the best features of several models, such as Long Short-Term Memory networks, Recurrent Neural Networks, Gated Recurrent Units, and Convolutional Neural Networks to address these issues. We can estimate AQI values more reliably and accurately through the integration of these models, which is essential for efficient urban development and environmental management. In addition, we present novel methods in data preprocessing to maximize our hybrid models computational effectiveness, guaranteeing real-time AQI forecasts while consuming the least amount of resources. We validate and experiment extensively on real-time pollutant datasets to show that our approach is superior to existing techniques. Our proposed hybrid DL models show significant increases in computing performance together with significantly greater prediction accuracy, which makes them suitable for use in smart communities where real-time decision-making is crucial. In summary, the present research advances the field of atmospheric monitoring and management by providing a practical and scalable method for AQI prediction in the setting of smart and sustainable cities. The hybrid DL models that have been suggested have enormous potential to facilitate preventative actions aimed at reducing air pollution and improving urban dweller’s quality of life when compared to single model. © 2025 selection and editorial matter, V. Subramaniyaswamy, G Revathy, Logesh Ravi, N. Thillaiarasu, and Naresh Kshetri; individual chapters, the contributors. |
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