| Title | Toward Cleaner Industries: Smart Cities' Impact on Predictive Air Quality Management |
|---|---|
| ID_Doc | 42972 |
| Authors | Chatterjee, K; Raju, M; Thara, MN; Reddy, MS; Priyadharshini, M; Selvamuthukumaran, N; Mallik, S; Alshahrani, HM; Abbas, M; Soufiene, B |
| Title | Toward Cleaner Industries: Smart Cities' Impact on Predictive Air Quality Management |
| Year | 2024 |
| Published | |
| Abstract | 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. |
| https://ieeexplore.ieee.org/ielx7/6287639/6514899/10540115.pdf |
Similar Articles
| ID | Score | Article |
|---|---|---|
40428
|
Kök, I; Simsek, MU; Özdemir, S A deep learning model for air quality prediction in smart cities(2017) | |
| 38505
|
Ghose, B; Rehena, Z; Anthopoulos, L A Deep Learning based Air Quality Prediction Technique Using Influencing Pollutants of Neighboring Locations in Smart City(2022)Journal Of Universal Computer Science, 28, 8 | |
| 42312
|
Honarvar, AR; Sami, A Towards Sustainable Smart City by Particulate Matter Prediction Using Urban Big Data, Excluding Expensive Air Pollution Infrastructures(2019) | |
| 36851
|
Liu, LL; Zhang, Y Smart environment design planning for smart city based on deep learning(2021) | |
| 44722
|
Schürholz, D; Kubler, S; Zaslavsky, A Artificial intelligence-enabled context-aware air quality prediction for smart cities(2020) | |
| 38552
|
Neo, EX; Hasikin, K; Lai, KW; Mokhtar, MI; Azizan, MM; Hizaddin, HF; Razak, SA; Yanto Artificial intelligence-assisted air quality monitoring for smart city management(2023) | |
| 42305
|
Kataria, A; Puri, V AI- and IoT-based hybrid model for air quality prediction in a smart city with network assistance(2022)Iet Networks, 11, 6 | |
| 40211
|
Bekkar, A; Hssina, B; Douzi, S; Douzi, K Air-pollution prediction in smart city, deep learning approach(2021)Journal Of Big Data, 8, 1 | |
| 41407
|
Elbaz, K; Hoteit, I; Shaban, WM; Shen, SL Spatiotemporal air quality forecasting and health risk assessment over smart city of NEOM(2023) | |
| 39260
|
Khan, MA; Kim, HC; Park, H Leveraging Machine Learning for Fault-Tolerant Air Pollutants Monitoring for a Smart City Design(2022)Electronics, 11, 19 |