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| 001 | 820483 | ||
| 005 | 20220729133743.0 | ||
| 024 | 7 | _ | |2 ISSN |a 1464-7141 |
| 024 | 7 | _ | |2 ISSN |a 1465-1734 |
| 024 | 7 | _ | |2 SCOPUS |a SCOPUS:2-s2.0-85110473857 |
| 024 | 7 | _ | |2 WOS |a WOS:000657782500001 |
| 024 | 7 | _ | |2 datacite_doi |a 10.18154/RWTH-2021-05636 |
| 024 | 7 | _ | |2 doi |a 10.2166/hydro.2021.023 |
| 037 | _ | _ | |a RWTH-2021-05636 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 690 |
| 100 | 1 | _ | |a Jiang, Jiping |b 0 |e Corresponding author |
| 245 | _ | _ | |a A hybrid wavelet-Lyapunov exponent model for river water quality forecast |h online |
| 260 | _ | _ | |a London |b IWA Publ. |c 2021 |
| 336 | 7 | _ | |0 0 |2 EndNote |a Journal Article |
| 336 | 7 | _ | |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |a Journal Article |b journal |m journal |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 DRIVER |a article |
| 336 | 7 | _ | |2 DataCite |a Output Types/Journal article |
| 336 | 7 | _ | |2 ORCID |a JOURNAL_ARTICLE |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: publications.rwth-aachen.de |
| 591 | _ | _ | |a Germany |
| 591 | _ | _ | |a India |
| 591 | _ | _ | |a Peoples R China |
| 700 | 1 | _ | |a Tang, Sijie |b 1 |
| 700 | 1 | _ | |a Liu, Rentao |b 2 |
| 700 | 1 | _ | |a Sivakumar, Bellie |b 3 |
| 700 | 1 | _ | |0 P:(DE-82)820499 |a Wu, Xiaoye |b 4 |u rwth |
| 700 | 1 | _ | |a Pang, Tianrui |b 5 |
| 773 | _ | _ | |0 PERI:(DE-600)2020923-X |a 10.2166/hydro.2021.023 |n 4 |p 864-878 |t Journal of hydroinformatics : JHI |v 23 |x 1465-1734 |y 2021 |
| 856 | 4 | _ | |u https://publications.rwth-aachen.de/record/820483/files/820483.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2021 |
| 915 | 1 | _ | |0 StatID:(DE-HGF)0031 |2 StatID |a Peer reviewed article |x 0 |
| 915 | _ | _ | |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |a Creative Commons Attribution CC BY 4.0 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0030 |2 StatID |a Peer Review |b ASC |d 2021-01-28 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0100 |2 StatID |a JCR |b J HYDROINFORM : 2019 |d 2021-01-28 |
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