Predictive Modeling of Environmental Parameters Using Ensemble Machine Learning Techniques

Air quality monitoring plays a major role in safeguarding public health as well as environmental sanity. Present air quality monitoring approaches usually depend on apparatus and their high costs may hinder their availability and use. In this research, we propose a new machine learning based approach to estimate an air quality index (AQI). Specifically, we explore the application of random forests (RF), gradient boosting (GB) and majority voting proof (VC) for AQI estimation from data comprising several different environmental variables. We begin by preparing data for cleaning and normalization. There are specific features that define corresponding AQI predictors. Then we trained several learning machines including RF, GB and VC in order to assess the performance of AQI prediction. The model’s performance is evaluated on a competition table basis. This enables hyperparameters adjustment via the competition table hence helps identify the best setup model. AQI prediction performance.The primary findings were that random forest models as well as gradient boosting have good predictive power while this system gains more strength when combining all three models together; vote-based one. These findings suggest that machine learning has great potential for improving air pollution control measures throughout broader sectors across the globe which will also increase its efficiency in several other goods/services areas including energy conservation programs.