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ACBiGRU-DAO: Attention Convolutional Bidirectional Gated Recurrent Unit-based Dynamic Arithmetic Optimization for Air
Vinoth Panneerselvam1, Revathi Thiagarajan2
1Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India. vinoth.ttk@gmail.com.
This study introduces a new air quality prediction model, the Attention Convolutional Bidirectional Gated Recurrent Unit based Dynamic Arithmetic Optimization (ACBiGRU-DAO), achieving 95.34% accuracy. The model effectively predicts air quality and classifies pollution levels for better environmental and health management.
Area of Science:
- Environmental Science
- Computer Science
- Data Science
Background:
- Air pollution is a significant environmental and health concern, especially in developing nations.
- Accurate air quality assessment is crucial for urban and industrial monitoring.
- Existing prediction models require enhancement for improved accuracy.
Purpose of the Study:
- To propose a novel air quality prediction model, ACBiGRU-DAO.
- To enhance prediction accuracy using Attention Convolutional Bidirectional Gated Recurrent Unit (ACBiGRU) and Dynamic Arithmetic Optimization (DAO).
- To classify air quality into six severity stages.
Main Methods:
- Utilized Indian air quality data including AQI, PM2.5, PM10, CO, NO2, SO2, and O3.
- Preprocessed data through missing value imputation and data transformation.
- Developed and optimized the ACBiGRU-DAO model for prediction and classification.
Main Results:
- The ACBiGRU-DAO approach achieved an accuracy of 95.34%.
- Demonstrated superior performance compared to other evaluated methods.
- Successfully predicted air quality and classified it into six AQI stages.
Conclusions:
- The proposed ACBiGRU-DAO model offers a highly accurate solution for air quality prediction.
- This approach can aid in monitoring and mitigating air pollution impacts.
- Effective air quality prediction is vital for public health and environmental protection.

