Deep Learning Applications in Air Pollution Control: A Comprehensive Review
- سال انتشار: 1402
- محل انتشار: سومین کنفرانس بین المللی شهر هوشمند، چالش ها و راهبردها
- کد COI اختصاصی: SMARTCITYC03_096
- زبان مقاله: فارسی
- تعداد مشاهده: 57
نویسندگان
Caspian Faculty of Engineering, College of Engineering, University of Tehran, P. O. Box: ۱۱۹-۴۳۸۴۱, Rezvanshahr, Iran
Caspian Faculty of Engineering, College of Engineering, University of Tehran, P. O. Box: ۱۱۹-۴۳۸۴۱, Rezvanshahr, Iran Burn and Regenerative Medicine Research Center, Guilan University of Medical Sciences, Rasht, Iran
Department of Environmental Science and Engineering, Faculty of Natural Resources, University of Guilan, Rasht, Iran
چکیده
This study delves into the transformative role of deep learning and neural networks in the domain of air pollution control. By focusing on enhanced detection and monitoring, particularly through convolutional and recurrent neural architectures, the research highlights the potential of these technologies to unravel complex patterns within air quality dynamics. Beyond mere detection, these models demonstrate proactive capabilities, enabling the prediction and forecasting of pollution events. This foresight empowers the implementation of adaptive control strategies, effectively minimizing health risks and optimizing resource allocation. However, the study acknowledges challenges related to data quality and interpretability, emphasizing the necessity for interdisciplinary collaboration among machine learning experts, environmental scientists, and policymakers. In synthesizing these findings, the research contributes to the advancement of sustainable strategies for mitigating the impact of air pollution on human health and the environment and also review methods of controlling it by deep learning approaches.کلیدواژه ها
Deep Learning, Neural Network, Air Pollution, Pollution Detectionاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.