TY - RPRT U1 - Forschungsbericht A1 - Rebolledo Coy, Margarita Alejandra A1 - Bartz-Beielstein, Thomas T1 - Modelling Zero-inflated Rainfall Data through the Use of Gaussian Process and Bayesian Regression N2 - 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. T3 - CIplus - 5/2018 KW - Data Analysis KW - Data Modelling KW - Bayesian Regression KW - Gaussian Process Y2 - 2018 U6 - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-7832 UN - https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-7832 SP - 3 S1 - 3 ER -