This study describes an application of hybrid modelling for an atmospheric variable in the Krsko basin.The hybrid model is a combination of a physics-based and data-driven model and has some properties of both modelli...This study describes an application of hybrid modelling for an atmospheric variable in the Krsko 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 Krsko 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.展开更多
文摘This study describes an application of hybrid modelling for an atmospheric variable in the Krsko 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 Krsko 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.
