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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2025-01-01
|
| Series: | Environmental Data Science |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460224000487/type/journal_article |