A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy

Occurrences of extreme events, especially floods, have become more frequent and severe in the recent past due to the global impacts of climate change. In this context, possibilities for generating a near‐accurate streamflow forecast at higher lead times, which could be utilized for developing a reli...

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Veröffentlicht in:	Water resources research Jg. 59; H. 2
Hauptverfasser:	Roy, Abhinanda, Kasiviswanathan, K. S., Patidar, Sandhya, Adeloye, Adebayo J., Soundharajan, Bankaru‐Swamy, Ojha, Chandra Shekhar P.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Washington John Wiley & Sons, Inc 01.02.2023
Schlagworte:	Accuracy Algorithms Climate change Environmental impact Error correction Error correction & detection Flood forecasting Flood warning systems Floods Forecast accuracy Frameworks HBV model hybrid model India Lead time Machine learning Mathematical models Model accuracy model validation Modelling Nepal particle filter Physics random forest River basins Rivers Stream discharge Stream flow streamflow forecast Streamflow forecasting Uncertainty uncertainty quantification Warning systems water watersheds Nepal India
ISSN:	0043-1397, 1944-7973
Online-Zugang:	Volltext
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