Streamflow prediction using artificial neural networks and soil moisture proxies

Streamflow prediction using artificial neural networks and soil moisture proxies

Machine learning models have been used extensively in hydrology, but issues persist with regard to their transparency, and there is currently no identifiable best practice for forcing variables in streamflow or flood modeling. In this paper, using data from the Centre for Ecology & Hydrology’s N...

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Bibliographic Details
Main Authors: Robert Edwin Rouse, Doran Khamis, Scott Hosking, Allan McRobie, Emily Shuckburgh
Format: Article
Language:English
Published: Cambridge University Press 2025-01-01
Series:Environmental Data Science
Subjects:
artificial neural networks
hydrology
machine learning
streamflow
Online Access:https://www.cambridge.org/core/product/identifier/S2634460224000487/type/journal_article
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