Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 8
Articles | Volume 25, issue 8
https://doi.org/10.5194/hess-25-4373-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-4373-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 8
Research article
 | 
11 Aug 2021
Research article |  | 11 Aug 2021

Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling

Herath Mudiyanselage Viraj Vidura Herath, Jayashree Chadalawada, and Vladan Babovic

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Short summary
Existing hydrological knowledge has been integrated with genetic programming based on a machine learning algorithm (MIKA-SHA) to induce readily interpretable distributed rainfall–runoff models. At present, the model building components of two flexible modelling frameworks (FUSE and SUPERFLEX) represent the elements of hydrological knowledge. The proposed toolkit captures spatial variabilities and automatically induces semi-distributed rainfall–runoff models without any explicit user selections.
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