Sparse and hybrid modelling of relative humidity: the Krško basin case study
| Autor: | Juš Kocijan, Matija Perne, Boštjan Grašic, Marija Zlata Božnar, Primož Mlakar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
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 Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765 |
| Zdroj: | CAAI Transactions on Intelligence Technology (2019) |
| Druh dokumentu: | article |
| ISSN: | 2468-2322 |
| DOI: | 10.1049/trit.2019.0054 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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