Towards Interpretable Physical‐Conceptual Catchment‐Scale Hydrological Modeling Using the Mass‐Conserving‐Perceptron

We investigate the applicability of machine learning technologies to the development of parsimonious, interpretable, catchment‐scale hydrologic models using directed‐graph architectures based on the mass‐conserving perceptron (MCP) as the fundamental computational unit. Here, we focus on architectur...

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Vydáno v:	Water resources research Ročník 60; číslo 10
Hlavní autoři:	Wang, Yuan‐Heng, Gupta, Hoshin V.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Washington John Wiley & Sons, Inc 01.10.2024 Wiley
Témata:	architectural hypotheses Base flow Catchments catchment‐scale Computer applications conceptual rainfall‐runoff (CRR) models Data search Diagnostic systems Dynamics Flow paths Groundwater hydrodynamics hydrograph Hydrologic models Hydrology Learning algorithms Machine learning MCP (mass‐conserving‐perceptron) model validation Modelling physically‐interpretable Regional development Representations River basins Training Watersheds
ISSN:	0043-1397, 1944-7973
On-line přístup:	Získat plný text
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