Journal of Artificial Intelligence and Capsule Networks
IRO Journals
Volume 8 — Issue 1 — March 2026

A Hybrid Deep Learning Framework for Air Quality Index Forecasting

https://doi.org/10.36548/jaicn.2026.1.003
Pages: 42-73
PDF
Saranya K G.
Associate Professor, Department of Computer Science, PSG College of Technology, Coimbatore, India
Akalya A.
PG Scholar, Department of Computer Science, PSG College of Technology, Coimbatore, India
Harini G.
PG Scholar, Department of Computer Science, PSG College of Technology, Coimbatore, India
March 2026
Volume 8 — Issue 1
Journal of Artificial Intelligence and Capsule Networks
PDF

How to Cite

K G., Saranya, Akalya A., and Harini G. 2026. “A Hybrid Deep Learning Framework for Air Quality Index Forecasting”. Journal of Artificial Intelligence and Capsule Networks 8 (1): 42-73. https://doi.org/10.36548/jaicn.2026.1.003.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— Air Quality Index
— Feature Selection
— Recurrent Neural Networks
— Quantum-Inspired Genetic Algorithm
— XGBoost
— PCA
Published: 23-03-2026

Abstract

The Air Quality Index (AQI) must be accurately and highly evaluated to limit the effects when exposed to air pollution on individual health and the ecosystem. The traditional techniques are used to predict air quality ineffective in managing the nature of the environmental data being analyzed and minimizing the effects of the most significant features of large numbers of dimensions. The purpose of this work is to address these issues using the creation of a hybrid deep learning algorithm implementing Feature Selection Techniques, Recurrent Neural Networks and a Quantum-Inspired Genetic Algorithm (QIGA).  The eXtreme Gradient Boosting (XGBoost) will be applied to the environmental dataset to evaluate importance of features. Additionally, the Principal Component Analysis (PCA) will reduce the dataset's dimensionality using most significant features from the original dataset as inputs for the prediction model. Recurrent Neural Networks (RNNs) are able to detect time-variant patterns of air quality (i.e., the pattern changes over time) based on the use of controlled memory. The use of quantum-based methodologies will allow rapid searches over high-dimensional datasets resulting higher performance than traditional optimization methods. This innovative methodology will lead to improved accuracy, computational efficiency and interpretability of AQI predictions. This method will be the basis for smart environmental monitoring systems used by researchers, policymakers and urban planners to make innovative decisions. Finally, the flexibility of the system makes it possible for users to apply it in any other forecasting-based environmental issues showing the scalability and efficiency.

References

  1. Wang, Jingyang, Xiaolei Li, Lukai Jin, Jiazheng Li, Qiuhong Sun, and Haiyao Wang. "An Air Quality Index Prediction Model Based on CNN-ILSTM." Scientific Reports 12, no. 1 (2022): 8373.
  2. Méndez, Manuel, Mercedes G. Merayo, and Manuel Núñez. "Machine Learning Algorithms to Forecast Air Quality: A Survey." Artificial intelligence review 56, no. 9 (2023): 10031-10066.
  3. Kalantari, Elham, Hamid Gholami, Hossein Malakooti, Ali Reza Nafarzadegan, and Vahid Moosavi. "Machine Learning for Air Quality Index (AQI) Forecasting: Shallow Learning or Deep Learning?." Environmental Science and Pollution Research 31, no. 54 (2024): 62962-62982.
  4. Li, Xiang, Ling Peng, Yuan Hu, Jing Shao, and Tianhe Chi. "Deep Learning Architecture for Air Quality Predictions." Environmental Science and Pollution Research 23, no. 22 (2016): 22408-22417.
  5. Zhang, Yang, Marc Bocquet, Vivien Mallet, Christian Seigneur, and Alexander Baklanov. "Real-Time Air Quality Forecasting, Part I: History, Techniques, and Current Status." Atmospheric environment 60 (2012): 632-655.
  6. Madan, Tanisha, Shrddha Sagar, and Deepali Virmani. "Air Quality Prediction Using Machine Learning Algorithms–A Review." In 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), IEEE, 2020, 140-145.
  7. Freeman, Brian S., Graham Taylor, Bahram Gharabaghi, and Jesse Thé. "Forecasting Air Quality Time Series Using Deep Learning." Journal of the Air & Waste Management Association 68, no. 8 (2018): 866-886., doi: 10.1109/ACCESS.2019.2903901.
  8. Aggarwal, Apeksha, and Durga Toshniwal. "A Hybrid Deep Learning Framework for Urban Air Quality Forecasting." Journal of Cleaner Production 329 (2021): 129660.
  9. Du, Shengdong, Tianrui Li, Yan Yang, and Shi-Jinn Horng. "Deep Air Quality Forecasting Using Hybrid Deep Learning Framework." IEEE Transactions on Knowledge and Data Engineering 33, no. 6 (2019): 2412-2424.
  10. Xiao, Fei, Mei Yang, Hong Fan, Guanghui Fan, and Mohammed AA Al-Qaness. "An Improved Deep Learning Model for Predicting Daily PM2. 5 Concentration." Scientific reports 10, no. 1 (2020): 20988.
  11. Sarkar, Nairita, Rajan Gupta, Pankaj Kumar Keserwani, and Mahesh Chandra Govil. "Air Quality Index Prediction Using an Effective Hybrid Deep Learning Model." Environmental Pollution 315 (2022): 120404.
  12. Zhou, Yanlai, Fi-John Chang, Li-Chiu Chang, I-Feng Kao, and Yi-Shin Wang. "Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts." Journal of cleaner production 209 (2019): 134-145.
  13. Jin, Xue-Bo, Nian-Xiang Yang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, and Jian-Lei Kong. "Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction." Mathematics 8, no. 2 (2020): 214.
  14. Kolehmainen, Mikko, Hannu Martikainen, Teri Hiltunen, and Juhani Ruuskanen. "Forecasting Air Quality Parameters Using Hybrid Neural Network Modelling." Environmental Monitoring and Assessment 65, no. 1 (2000): 277-286.
  15. Shen, Mengqiu, Anfeng Liu, Guosheng Huang, Neal N. Xiong, and Huimin Lu. "ATTDC: An Active and Traceable Trust Data Collection Scheme for Industrial Security in Smart Cities." IEEE Internet of Things journal 8, no. 8 (2021): 6437-6453.