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...

Full description

Saved in:

Bibliographic Details
Published in:	Water resources research Vol. 56; no. 5
Main Authors:	Schmidt, Lennart, Heße, Falk, Attinger, Sabine, Kumar, Rohini
Format:	Journal Article
Language:	English
Published:	Washington John Wiley & Sons, Inc 01.05.2020
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!