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Dataset . 2024
License: CC BY
Data sources: Datacite
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Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 6

Authors: Gupta, Abhinav; Hantush, Mohamed; Govindaraju, Rao;

Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 6

Abstract

This resource contains data corresponding to the study titled "Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures" A detailed description of the dataset is contained in a readme file contained in the resource. This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 0% outliers were allowed. For further information on this resource, please contact abhigupta.1611@gmail.com

Related Organizations
Keywords

Limits-of-acceptability, Modeling, Hydrology, SAC-SMA

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average