Air Quality Index Prediction Model Utilizing Random Forest and NodeMCU

Air pollution is a silent public health crisis demanding smarter solutions. This study discusses one such novelty in the use of machine learning and IoT for the prediction of hyper-local AQI forecasting. For this purpose, we will be integrating the Random Forest algorithm with NodeMCU to obtain point accuracy in AQI forecasts. It basically consists of a strong machine learning model trained on historical data with rich environmental parameters, such as air quality indicators including PM2.5 and ozone, along with meteorological data. In this way, the model will be able to forecast future AQI levels with very high accuracy. Real-time data acquisition is facilitated by NodeMCU, an IoT platform that can act like an environmental sentinel. It feeds the model real-time data through its live feed. Consequently, it guarantees real-time prediction of AQI with location specificity. The model is crossvalidated at large, returning a promising accuracy level of 99%, hence making it a potential revolutionist in air quality management. With machine learning and IoT combined, this work opens a gateway toward a future wherein proactive air quality management enables greener cities and a healthier future.