ENVIRONMENTAL MONITORING AND AIR POLLUTION PREDICTION USING ML TECHNIQUES

Abstract

The objective of this initiative is to improve the assessment of air quality and protect public health by utilizing machine learning techniques for environmental monitoring and air pollution prediction. Accurate air quality forecasting is imperative for long-term development, as pollution has increased as a result of urbanization, industry, and automotive emissions. The proposed research would employ machine learning algorithms, including the Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Networks, and Long Short-Term Memory (LSTM) networks, to analyze current and historical environmental data collected by sensors and monitoring stations. Temperature, humidity, CO, SO2, NO2, and particulate matter (PM2.5, PM10) are among the atmospheric factors that are employed for prediction and analysis. Approaches such as data pretreatment, feature selection, and model enhancement are employed to enhance prediction accuracy while reducing computer complexity. Experiments demonstrate that advanced machine learning models are more accurate and effective in predicting air pollution levels than traditional statistical methods. Our research has brought smart city applications, early warning systems, and pollution management strategies one step closer to becoming a reality, thereby fostering healthier and cleaner communities.