Machine learning for postprocessing ensemble streamflow forecasts

Machine learning for postprocessing ensemble streamflow forecasts

Skillful streamflow forecasts can inform decisions in various areas of water policy and management. We integrate numerical weather prediction ensembles, distributed hydrological model, and machine learning to generate ensemble streamflow forecasts at medium-range lead times (1–7 days). We demonstrat...

Full description

Bibliographic Details
Main Authors: Sanjib Sharma, Ganesh Raj Ghimire, Ridwan Siddique
Format: Article
Language:English
Published: IWA Publishing 2023-01-01
Series:Journal of Hydroinformatics
Subjects:
distributed hydrologic model
ensemble streamflow forecast
lstm
machine learning
numerical weather prediction
postprocessor
Online Access:http://jhydro.iwaponline.com/content/25/1/126

Internet

http://jhydro.iwaponline.com/content/25/1/126

Similar Items