Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework

Hydrologic signatures are quantitative metrics that describe a streamflow time series. Examples include annual maximum flow, baseflow index and recession shape descriptors. In this paper, we use machine learning (ML) to learn encodings that are optimal ML equivalents of hydrologic signatures, and th...

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Veröffentlicht in:	Water resources research Jg. 59; H. 3
Hauptverfasser:	Botterill, Tom E., McMillan, Hilary K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Washington John Wiley & Sons, Inc 01.03.2023
Schlagworte:	Base flow Climate Climatic data Coders Coefficients data collection deep learning Dynamics encoder–decoder flow indices Flow mapping flow metric Hydrologic models hydrologic signature Hydrology Information processing Learning algorithms Machine learning Maximum flow meteorological data Model accuracy Modelling Neural networks Outflow Precipitation prediction Rainfall-runoff relationships Runoff Runoff models Seasonal variations Seasonality Sensitivity analysis Signatures Stream discharge Stream flow time series analysis Watersheds
ISSN:	0043-1397, 1944-7973
Online-Zugang:	Volltext
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