Air polution other research.md

Recent studies have explored various machine learning and deep learning approaches to predict air quality, achieving notable performance metrics:

1. Optimized Machine Learning Model for AQI Prediction in Indian Cities (2023):

   • Method: Combined Grey Wolf Optimization with Decision Tree algorithms.
   • Performance: Achieved accuracy rates of 88.98% for New Delhi, 91.49% for Bangalore, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai, and 97.68% for Visakhapatnam. (pmc.ncbi.nlm.nih.gov)

2. AirPhyNet: Physics-Guided Neural Network for Air Quality Prediction (2024):

   • Method: Integrated physics principles of air particle movement into a deep learning framework.
   • Performance: Demonstrated superior accuracy in lead times up to 72 hours, with reductions in Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) by 3.7% and 6.1%, respectively, compared to other methods. (arxiv.org)

3. Deep Learning Techniques for Air Quality Prediction (2021):

   • Method: Employed deep learning neural networks.
   • Performance: Reported improved prediction performance over traditional machine learning techniques. (migrationletters.com)

4. AirNet: Predictive Machine Learning Model for Air Quality Forecasting (2024):

   • Method: Developed a predictive machine learning model.
   • Performance: Demonstrated remarkable accuracy in predicting global temperature trends, achieving high testing performance accuracy of 100%. (environmentalsystemsresearch.springeropen.com)

5. Spatiotemporal Graph Convolutional Recurrent Neural Network Model for Citywide Air Pollution Forecasting (2023):

   • Method: Integrated Graph Convolutional Networks into a Recurrent Neural Network structure to capture spatiotemporal features.
   • Performance: Outperformed state-of-the-art ConvLSTM models in air pollution prediction with a smaller number of parameters. (arxiv.org)

These studies highlight the effectiveness of combining traditional machine learning algorithms with optimization techniques, as well as integrating physics principles into deep learning frameworks, to enhance air quality prediction accuracy.