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.
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.
the Creative Commons Attribution 4.0 License.
Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling
Herath Mudiyanselage Viraj Vidura Herath
Department of Civil and Environmental Engineering, National University
of Singapore, Singapore 117576, Singapore
Jayashree Chadalawada
Department of Civil and Environmental Engineering, National University
of Singapore, Singapore 117576, Singapore
Department of Civil and Environmental Engineering, National University
of Singapore, Singapore 117576, Singapore
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Latest update: 23 Apr 2024
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.
Existing hydrological knowledge has been integrated with genetic programming based on a machine...