4 hasil untuk "Jonathan Frame"
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Artikel Jurnal
Time fractional Saint Venant equations reveal the physical basis of hydrograph retardation through model comparison and field data
Abstract River hydrodynamics are influenced by numerous factors that traditional models often fail to fully capture. Simulating complex hydrographs can benefit from parsimonious upscaling models, such as fractional derivative equations, that reduce the need to account for all variables. While…
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Artikel Jurnal Teks lengkap
Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics
Abstract We build three long short‐term memory (LSTM) daily streamflow prediction models (deep learning networks) for 531 basins across the contiguous United States (CONUS), and compare their performance: (1) a LSTM post‐processor trained on the United States National Water Model (NWM) outputs…
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Artikel Jurnal Teks lengkap
What Role Does Hydrological Science Play in the Age of Machine Learning?
Abstract This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall‐runoff simulation indicate that there is significantly more information in large‐scale hydrological data sets than…
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Artikel Jurnal
Deep learning rainfall–runoff predictions of extreme events
The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that the predictive accuracy of data-driven models based on deep learning may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis…