FORECASTING AIR POLLUTION LEVELS WITH MACHINE LEARNING TECHNIQUES

The capacity to inhale unpolluted air is a fundamental aspect of citizenship; thus, it is the duty of every individual to implement all necessary measures to preserve it. The principal technique employed for early warning and pollution management is research centered on air quality forecasting. To assess air quality, we advise AS nursing students to predominantly utilize a machine learning framework grounded in the Sunshine GBM model. This model, a trained lightweight GBM classifier for victims, enhances the precision of air quality predictions by integrating meteorological data from several sources. It achieves this by utilizing all accessible abstracted data. Historical air quality data, current monitoring stations, and satellite meteorological information are integrated to predict future air pollution patterns. The approach predicted that the Associate in Nursing will achieve 92% accuracy.