Improving Health Outcomes Through Transfer Learning and LSTM-Driven Air Quality Prediction

To forecast air quality, this study offers a robust deep learning model that combines Long short-term memory networks (LSTM), Convolutional neural networks (CNN), and transfer learning. The model’s usefulness was evaluated using a variety of critical performance indicators, including Root Squared(RS), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Cross Validation. The investigation’s findings indicate that exceptionally precise and accurate air quality forecasts are possible. The model yielded values that were closely aligned with projections with a reasonably small Mean Absolute Error (MAE) of 1.2 g/m3. The model’s average Root Mean Square Error (RMSE), is 1.6 g/m3, showing its overall quality. This is due to the precision and accuracy of the model. Furthermore, the Root squared(RS) score, which has a high correlation and an average value of 0.89, may account for 89% of the variance in air quality data. This demonstrates that the model accurately captures the underlying trends and patterns in the dynamics of air quality. Cross Validation findings that are consistent across different data subsets confirm the model’s high generalization and resilience. These discoveries have far-reaching ramifications for industry, environmental management, and public health. Accurate air quality forecasts enable individuals and governments to take proactive measures to protect public health, optimize environmental mitigation programs, and improve community welfare. Because of the model’s exceptional performance, it is possible to improve both public health and environmental quality.