Modelling Zero-inflated Rainfall Data through the Use of Gaussian Process and Bayesian Regression
- Rainfall is a key parameter for understanding the water cycle. An accurate rainfall measurement is vital in the development of hydrological models. By means of indirect measurement, satellites can nowadays estimate the rainfall around the world. However, these measurements are not always accurate. As a first approach to generate a bias-corrected rainfall estimate using satellite data, the performance of Gaussian process and Bayesian regression is studied. The results show Gaussian process as the better option for this dataset but leave place to improvements on both modelling strategies.
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| Author: | Margarita Alejandra Rebolledo Coy, Thomas Bartz-Beielstein |
|---|---|
| URN: | urn:nbn:de:hbz:832-cos4-7832 |
| Series (Serial Number): | CIplus (5/2018) |
| Document Type: | Report |
| Language: | English |
| Year of Completion: | 2018 |
| Release Date: | 2018/11/19 |
| Tag: | Bayesian Regression; Data Analysis; Data Modelling; Gaussian Process |
| Page Number: | 3 |
| Institutes and Central Facilities: | Fakultät für Informatik und Ingenieurwissenschaften (F10) |
| CCS-Classification: | G. Mathematics of Computing |
| H. Information Systems | |
| Dewey Decimal Classification: | 000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik |
| Open Access: | Open Access |
| Licence (German): |  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
