EcoAI Forecast: Multi - Model Air Quality Prediction in Patancheru

In the face of escalating air pollution in Patancheru, Hyderabad, the ‘EcoAI Forecast’ project presents a pioneering approach. It transcends the limitations of traditional linear models, which fail to effectively map the intricacies of air quality fluctuations. This research introduces a comprehensive multi-model machine learning framework, integrating sophisticated algorithms like LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), CNN (Convolutional Neural Network), and an Ensemble model. These models are adept at processing complex environmental datasets, particularly focusing on the nuanced prediction of PM2.5 levels, a crucial air pollutant. Utilizing a dataset enriched with hourly PM2.5 measurements, the study illustrates a paradigm shift from conventional methodologies to more dynamic, data-driven techniques. The Ensemble model, a centerpiece of this research, demonstrates exceptional performance, achieving an RMSE (Root Mean Square Error) of 0.068 and an R2 (Coefficient of Determination) of 0.945. These metrics not only signify a substantial improvement over traditional models but also underscore the Ensemble model's precision in forecasting air quality. The success of the ‘EcoAI Forecast’ project paves the way for future advancements in environmental monitoring, exemplifying the transformative potential of advanced machine learning techniques in urban air quality prediction and public health preservation.