Articles | Volume 26, issue 2
Research article
31 Jan 2022
Research article |  | 31 Jan 2022

Regionalization of hydrological model parameters using gradient boosting machine

Zhihong Song, Jun Xia, Gangsheng Wang, Dunxian She, Chen Hu, and Si Hong

Model code and software

Songzh101/GBM_HydroModel_Regionalization: The regionalization of hydrological model Z. H. Song

Short summary
We performed a machine learning approach to regionalize the parameters of a China-wide hydrological model by linking six model parameters with 10 physical attributes (terrain and soil properties). The results show the superiority of machine-learning-based regionalization approach compared with the traditional linear regression method in ungauged regions. We also obtained the relative importance of attributes against model parameters.