Development of Machine Learning Models for the Nearcasting of the Duration of Low-Visibility Events in the Indo-Gangetic Plains Regions of India

The prediction of the duration of the occurrence of low-visibility events in a calendar day is a difficult process because of the complex and chaotic mechanisms of the onset and dissipation of the low-visibility events. However, it is most useful for the operation of airport services (scheduling of aircraft, optimal operations of the airports) and the planning of any activities (travel, tourism, agriculture, etc.). This research tries to build the best dynamic weighted ensemble of the best combination of base machine learning( ML) models (Light Gradient Boosting Machine (Light GBM), Random Forest (RF), and Support Vector Regression (SVR)) to accurately nearcast the duration of low-visibility events( fog (surface visbility