Optimized Neural Network Framework for Air Quality Index Prediction Using Adaptive Error Minimization and Regression Performance Enhancement

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.