Optimized Neural Network Framework for Air Quality Index Prediction Using Adaptive Error Minimization and Regression Performance Enhancement | IJCT Volume 13 – Issue 4 | IJCT-V13I4P19
International Journal of Computer Techniques
ISSN 2394-2231
Author
Dr Asha N, Dr M P Indra Gandhi
Abstract
The climate, sustainable development, and human health are all greatly impacted by air pollution, which has become one of the most important environmental issues. Precise forecasting of the Air Quality Index (AQI) facilitates prompt actions, aids in environmental management, and helps legislators put into practice efficient pollution control measures. Traditional machine learning models often exhibit limitations in capturing the complex nonlinear relationships among atmospheric pollutants, leading to increased prediction errors and reduced regression performance. This research suggests a Artificial Neural Network-Based Error Optimization Algorithm (NNEOA) for precise AQI prediction with reduced error rate and improved regression fit in order to overcome the difficulties air pollution forcasting.
Keywords
Air Quality Index (AQI); Neural Network-Based Error Optimization Algorithm (NNEOA); Artificial Neural Network; Error Optimization; Regression Performance; Air Pollution Forecasting.
Conclusion
The experimental results show that training the model with several hidden-layer configurations for AQI prediction can successfully reduce the neural network’s error rate. The implementation of a multilayer neural network, employing deep learning techniques during the training and testing phases on the air pollution dataset, significantly improved prediction performance. With a Mean Absolute Error (MAE) of 1.1632 and an accuracy (R²) of 0.99569, the suggested model demonstrated outstanding agreement between the observed and predicted AQI values. Furthermore, the evaluation of the optimized multilayer neural network led to the development of a robust Neural Network Architecture for AQI Prediction, providing an effective framework for accurate air quality forecasting.
References
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How to Cite This Paper
Dr Asha N, Dr M P Indra Gandhi (2026). Optimized Neural Network Framework for Air Quality Index Prediction Using Adaptive Error Minimization and Regression Performance Enhancement. International Journal of Computer Techniques, 13(4). ISSN: 2394-2231.
Optimized Neural Network Framework for Air Quality Index Prediction Using Adaptive Error Minimization and Regression Performance EnhancementDownload
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