Department of Computer Science and Engineering, Nalla Malla Reddy Engineering College, Hyderabad, India
Muntha Raju
Department of Computer Science and Engineering, Nalla Malla Reddy Engineering College, Hyderabad, India
Machakanti Navya Thara
Department of Artificial Intelligence and Machine Learning, Nalla Malla Reddy Engineering College, Hyderabad, India
Mandadi Sriya Reddy
Department of Computer Science and Engineering, Nalla Malla Reddy Engineering College, Hyderabad, India
M. Priyadharshini
Department of Artificial Intelligence and Machine Learning, Nalla Malla Reddy Engineering College, Hyderabad, India
N. Selvamuthukumaran
Department of Computer Science and Engineering, Nalla Malla Reddy Engineering College, Hyderabad, India
Saurav Mallik
Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA
Haya Mesfer Alshahrani
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P. O. Box 84428, Riyadh, Saudi Arabia
Mohamed Abbas
Electrical Engineering Department, College of Engineering, King Khalid University, Abha, Saudi Arabia
The Smart City (SC) framework has garnered global recognition for its transformative influence on society through innovative solutions. However, the extensive use of Internet of Things (IoT) devices in SCs raises concerns regarding electronic waste and resource consumption. Addressing this challenge necessitates integrating smart grid systems to safeguard SC residents’ environment and well-being. Accurate air quality prediction is essential for informed societal decisions, safe transportation, and disaster preparedness. This study introduces a novel approach: Towards Cleaner Industries: Smart Cities’ Impact on Predictive Air Quality Management (SPAM). The SPAM model utilizes a bidirectional stacking mechanism of long short-term memory neural networks, considering spatiotemporal correlations to forecast future air pollutant concentrations. Surpassing conventional methods, SPAM model enhances accuracy while reducing computational complexity. Experimental findings demonstrate enhanced efficiency and accuracy, underscoring its practicality in industrial contexts. The SPAM model represents a significant advancement in promoting environmental sustainability within the SC framework.
