The Air Quality Prediction System for Pollution Mitigation and Public Health is an advanced analytical platform designed to forecast air quality levels using cutting-edge time series models. The project leverages ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory) neural networks to deliver precise and actionable predictions of air pollution levels.
The system's primary objectives are to:
Enhance Predictive Accuracy: Achieve up to 95% accuracy in forecasting air quality by implementing sophisticated ARIMA and LSTM models, allowing for precise anticipations of pollution trends.
Reduce Forecasting Error: Utilize advanced data preprocessing and model fine-tuning techniques to reduce prediction errors by 25%, ensuring reliable and actionable insights.
Support Environmental Policy: Provide valuable insights for environmental monitoring and policy-making by analyzing historical air quality data and forecasting future trends, aiding in proactive pollution management and urban planning.
Scalable and Adaptable Solution: Develop a solution that can be tailored to different geographical regions and pollutant types, supporting broader applications and integration into diverse environmental management systems.
The project represents a significant advancement in environmental analytics, aiming to mitigate the impacts of pollution and improve public health through accurate and actionable air quality forecasts.
