Sparse and hybrid modelling of relative humidity: the Krško basin case study

Abstract

This study describes an application of hybrid modelling for an atmospheric variable in the Krško basin. The hybrid model is a combination of a physics-based and data-driven model and has some properties of both modelling approaches. In the authors’ case, it is used for the modelling of an atmospheric variable, namely relative humidity in a particular location for the purpose of using the predictions of the model as an input to the air-pollution-dispersion model for radiation exposure. The presented hybrid model is a combination of a physics-based atmospherical model and a Gaussian-process (GP) regression model. The GP model is a probabilistic kernel method that also enables evaluation of prediction confidence. The problem of poor scalability of GP modelling was solved using sparse GP modelling; in particular, the fully independent training conditional method was used. Two different approaches to dataset selection for empirical model training were used and multiple-step-ahead predictions for different horizons were assessed. It is shown in this study that the accuracy of the predicted relative humidity in the Krško basin improved when using hybrid models over using the physics-based model alone and that predictions for a considerable length of horizon can be used.

Keywords

• environmental science computing
• air pollution
• learning (artificial intelligence)
• regression analysis
• gaussian processes
• humidity
• geophysics computing
• physics-based atmospherical model
• gaussian-process regression model
• gp model
• sparse gp modelling
• empirical model training
• physics-based model
• hybrid modelling
• relative humidity
• krško basin case study
• atmospheric variable
• data-driven model
• air-pollution-dispersion model