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Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term me...

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Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

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TN_cdi_doaj_primary_oai_doaj_org_article_52439268c3c94462808e3826ed5337bd

Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

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https://collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_52439268c3c94462808e3826ed5337bd

Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

Full title

Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

Publisher

Katlenburg-Lindau: Copernicus GmbH

Journal title

Hydrology and earth system sciences, 2021, Vol.25 (10), p.5517-5534

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_52439268c3c94462808e3826ed5337bd

Language

English

Formats

Publication information

Publisher

Katlenburg-Lindau: Copernicus GmbH

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SCOPE AND CONTENTS

Contents

Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have demonstrated the applicability of LSTM-based models for rainfall–runoff modelling; however, LSTMs have not been tested on c...

ALTERNATIVE TITLES

Full title

Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

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PRIMARY IDENTIFIERS

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_52439268c3c94462808e3826ed5337bd

Permalink

https://collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_52439268c3c94462808e3826ed5337bd

OTHER IDENTIFIERS

ISSN

1607-7938,1027-5606

E-ISSN

1607-7938

DOI

10.5194/hess-25-5517-2021

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