Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural N...
Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks
About this item
Full title
Author / Creator
Jiang, Wei , Dang, Xupeng and Zhang, Rui
Publisher
Basel: MDPI AG
Journal title
Language
English
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Publication information
Publisher
Basel: MDPI AG
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Scope and Contents
Contents
Regional rainfall-runoff modeling is a classic and significant research topic in hydrological sciences. Currently, the predominant modeling approach is developing data-driven models. This study proposes a rainfall-runoff model named ED-TimesNet (Encoder–Decoder-based TimesNet), which consists of convolutional neural networks. It transforms a one-di...
Alternative Titles
Full title
Empowering Regional Rainfall-Runoff Modeling Through Encoder–Decoder Based on Convolutional Neural Networks
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_proquest_journals_3165914850
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3165914850
Other Identifiers
ISSN
2073-4441
E-ISSN
2073-4441
DOI
10.3390/w17030339
