Challenges in Applying Machine Learning Models for Hydrological Inference: A Case Study for Flooding Events Across Germany

Machine learning (ML) algorithms are being increasingly used in Earth and Environmental modeling studies owing to the ever‐increasing availability of diverse data sets and computational resources as well as advancement in ML algorithms. Despite advances in their predictive accuracy, the usefulness o...

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Vydáno v:	Water resources research Ročník 56; číslo 5
Hlavní autoři:	Schmidt, Lennart, Heße, Falk, Attinger, Sabine, Kumar, Rohini
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Washington John Wiley & Sons, Inc 01.05.2020
Témata:	Accuracy Algorithms Artificial neural networks case studies Computer applications data collection Datasets Environment models Environmental modeling Flood predictions Flooding Floods Germany Heterogeneity Hydrologic models Hydrology Inference Learning algorithms Machine learning Model accuracy Modelling Neural networks prediction Regression analysis water Germany
ISSN:	0043-1397, 1944-7973
On-line přístup:	Získat plný text
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