Forecasting Air Quality Trends Using Long Short-Term Memory (LSTM) Networks

Air quality is a critical environmental factor affecting public health and well-being. prediction of air quality can enable timely interventions and effective management strategies. This project explores the use of Long Short-Term Memory (LSTM) networks for predicting future air quality based on historical sensor data. We collected data from MQ135, MQ2, and MQ4 sensors, which measure various air pollutants including CO2, methane, and particulate matter. The LSTM model, known for its capability to capture long-term dependencies in time-series data, was   employed to forecast air quality over the next few days. Our methodology involved preprocessing sensor data, constructing and tuning the LSTM model, and evaluating its performance using metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results demonstrated that the LSTM model effectively predicted air quality trends, achieving high accuracy and providing insights into future air pollution   levels. The findings suggest that LSTM networks are a promising tool for air quality forecasting, with potential applications in environmental monitoring and public health management.