Articles | Volume 25, issue 8
Hydrol. Earth Syst. Sci., 25, 4373–4401, 2021
https://doi.org/10.5194/hess-25-4373-2021
Hydrol. Earth Syst. Sci., 25, 4373–4401, 2021
https://doi.org/10.5194/hess-25-4373-2021

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 et al.

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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Cited articles

Addor, N., and Melsen, L. A.: Legacy, rather than adequacy, drives the selection of hydrological models, Water Resour. Res., 55, 378–390, https://doi.org/10.1029/2018WR022958, 2019. 
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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.