Ensemble causal modelling for frost forecast in vineyard

Abstract

Being a kind of natural phenomenon, frost occurrence is influenced by environment factors with an accumulated impact. The relation between environment factors and frost event is of cause-effect governed by a process taking place in time. Having cause-affect concerned, frost forecast is a problem of complex cause-effect rather than a complicated association and problem modelling plays a key role for the success of forecast. With limited data and lack of true physical model, a well-trained model by machine learning from data is only an approximation constructed on a sub-space of problem domain. As a continued study of causal modelling in frost forecast developed previously, this paper proposes an ensemble causal modelling to compensate the performance of individual models. Such an ensemble involves models with different length of time-delay so to provide a spectrum of early alarm of frost occurrence. Experiments are done using sensor data collected from a vineyard in Hokkaido, Japan.