Frost Forecast using Machine Learning - from association to causality

To effectively protect plants from frost damage, an early alarm of frost can be helpful for growers. Frost is a localized phenomenon and can be quite variable across a small area, so predictive models developed with local data are preferred. As a climate phenomenon the occurrence of frost is closely related to multiple environment factors including temperature, humidity, radiation and more. This article proposes construction of predictive models using support vector machine approach to capture possible causal relation between these factors and frost. Such models trained with specific local data are expected to help frost forecast in a few hours ahead in the local area. Problem analysis, modeling methodology, and model ensemble are discussed, and experiments with real data are provided.