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Smart city article details
| Title | Machine Learning Techniques For Spatio-Temporal Air Pollution Prediction To Drive Sustainable Urban Development In The Era Of Energy And Data Transformation |
|---|---|
| ID_Doc | 36027 |
| Authors | Zareba M.; Cogiel S.; Danek T.; Weglinska E. |
| Year | 2024 |
| Published | Energies , 17, 11 |
| DOI | http://dx.doi.org/10.3390/en17112738 |
| Abstract | Sustainable urban development in the era of energy and digital transformation is crucial from a societal perspective. Utilizing modern techniques for analyzing large datasets, including machine learning and artificial intelligence, enables a deeper understanding of historical data and the efficient prediction of future events based on data from IoT sensors. This study conducted a multidimensional historical analysis of air pollution to investigate the impacts of energy transformation and environmental policy and to determine the long-term environmental implications of certain actions. Additionally, machine learning (ML) techniques were employed for air pollution prediction, taking spatial factors into account. By utilizing multiple low-cost air sensors categorized as IoT devices, this study incorporated data from various locations and assessed the influence of neighboring sensors on predictions. Different ML approaches were analyzed, including regression models, deep neural networks, and ensemble learning. The possibility of implementing such predictions in publicly accessible IT mobile systems was explored. The research was conducted in Krakow, Poland, a UNESCO-listed city that has had long struggle with air pollution. Krakow is also at the forefront of implementing policies to prohibit the use of solid fuels for heating and establishing clean transport zones. The research showed that population growth within the city does not have a negative impact on PMx concentrations, and transitioning from coal-based to sustainable energy sources emerges as the primary factor in improving air quality, especially for PMx, while the impact of transportation remains less relevant. The best results for predicting rare smog events can be achieved using linear ML models. Implementing actions based on this research can significantly contribute to building a smart city that takes into account the impact of air pollution on quality of life. © 2024 by the authors. |
| Author Keywords | air pollution; big data; energy transition; machine learning; smart cities; urban development |
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