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New double decomposition deep learning methods for river water level forecasting.
Ahmed, A A Masrur; Deo, Ravinesh C; Ghahramani, Afshin; Feng, Qi; Raj, Nawin; Yin, Zhenliang; Yang, Linshan.
Affiliation
  • Ahmed AAM; School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia; Department of Infrastructure Engineering, The University of Melbourne, Victoria 3010, Australia. Electronic address: AbulAbrarMasrur.Ahmed@usq.edu.au.
  • Deo RC; School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia. Electronic address: ravinesh.deo@usq.edu.au.
  • Ghahramani A; Centre for Sustainable Agricultural Systems, University of Southern Queensland, Springfield, QLD 4300, Australia. Electronic address: afshin.ghahramani@usq.edu.au.
  • Feng Q; Key Laboratory of Ecohydrology of Inland River Basin, Chinese Academy of Sciences, China; Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd 320, Lanzhou 730000, Gansu Province, China. Electronic address: qifeng@lzb.ac.cn.
  • Raj N; Centre for Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia. Electronic address: nawin.raj@usq.edu.au.
  • Yin Z; Key Laboratory of Ecohydrology of Inland River Basin, Chinese Academy of Sciences, China; Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd 320, Lanzhou 730000, Gansu Province, China. Electronic address: yinzhenliang@lzb.ac.cn.
  • Yang L; Key Laboratory of Ecohydrology of Inland River Basin, Chinese Academy of Sciences, China; Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Donggang West Rd 320, Lanzhou 730000, Gansu Province, China. Electronic address: yanglsh08@lzb.ac.cn.
Sci Total Environ ; 831: 154722, 2022 Jul 20.
Article de En | MEDLINE | ID: mdl-35339552
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Rivières / Apprentissage profond Type d'étude: Prognostic_studies Pays/Région comme sujet: Oceania Langue: En Journal: Sci Total Environ Année: 2022 Type de document: Article
Texte intégral
Ajouter à My VHL
Imprimer
XML
PubMed Links
Recherche sur Google

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Rivières / Apprentissage profond Type d'étude: Prognostic_studies Pays/Région comme sujet: Oceania Langue: En Journal: Sci Total Environ Année: 2022 Type de document: Article