HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particul...
Uloženo v:
| Vydáno v: | Hydrology and earth system sciences Ročník 28; číslo 17; s. 4187 - 4201 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Katlenburg-Lindau
Copernicus GmbH
12.09.2024
Copernicus Publications |
| Témata: | |
| ISSN: | 1607-7938, 1027-5606, 1607-7938 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is common: training deep learning models on small, homogeneous data sets, typically data from only a single hydrological basin. In this position paper, we show that LSTM rainfall–runoff models are best when trained with data from a large number of basins. |
|---|---|
| AbstractList | Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is common: training deep learning models on small, homogeneous data sets, typically data from only a single hydrological basin. In this position paper, we show that LSTM rainfall–runoff models are best when trained with data from a large number of basins. Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for rainfall–runoff modeling. A large majority of studies that use this type of model do not follow best practices, and there is one mistake in particular that is common: training deep learning models on small, homogeneous data sets, typically data from only a single hydrological basin. In this position paper, we show that LSTM rainfall–runoff models are best when trained with data from a large number of basins. |
| Audience | Academic |
| Author | Nearing, Grey Gauch, Martin Kratzert, Frederik Klotz, Daniel |
| Author_xml | – sequence: 1 givenname: Frederik orcidid: 0000-0002-8897-7689 surname: Kratzert fullname: Kratzert, Frederik – sequence: 2 givenname: Martin orcidid: 0000-0002-4587-898X surname: Gauch fullname: Gauch, Martin – sequence: 3 givenname: Daniel orcidid: 0000-0002-9843-6798 surname: Klotz fullname: Klotz, Daniel – sequence: 4 givenname: Grey surname: Nearing fullname: Nearing, Grey |
| BookMark | eNp1kk9v1DAQxSNUJNrCB-BmiQs9pPhfYodbVRW60pZKJHfLcSapl4292F6g3x6HBcEikA8ejX7vaTTzzooT5x0UxUuCLyvS8DcPEGNJZcmJFCXFlD8pTkmNRSkaJk_-qJ8VZzFuMKZS1vS06G5v2hbd76yz3sW36AN8gYBS0NYhjdbeTah98CGVHYQZ3cHswyN6vW67uwvkIH314RPyCxqtm7aAep2L58XTUW8jvPj5nxfdu5vu-rZc379fXV-tS8MlSyWnlBiph4ZhqAzlUmppRjZWlBk8SFyJoWcN5kybmtOmzjAhmuGe0b4ZCTsvVgfbweuN2gU76_CovLbqR8OHSemQrNmCItmH1tUguBC8GlgPfJBUjKMECtDQ7PXq4LUL_vMeYlIbvw8uT68YwQwLigX7TU06m1o3-rwpM9to1JXEssKSUJ6py39Q-Q0wW5PvNtrcPxJcHAkyk-BbmvQ-RrVqPx6z4sCa4GMMMCpjk075esvRtopgtQRCLYFQVKolEGoJRFaSv5S_VvZ_zXdvSrXF |
| CitedBy_id | crossref_primary_10_3389_frwa_2025_1595898 crossref_primary_10_5194_hess_29_2811_2025 crossref_primary_10_1016_j_ejrh_2025_102748 crossref_primary_10_1016_j_envsoft_2025_106350 crossref_primary_10_1016_j_envsoft_2025_106691 crossref_primary_10_1016_j_ejrh_2025_102228 crossref_primary_10_1016_j_ecoinf_2025_102994 crossref_primary_10_1016_j_envsoft_2025_106696 crossref_primary_10_5194_hess_29_3145_2025 crossref_primary_10_5194_npg_31_535_2024 crossref_primary_10_1080_02626667_2025_2452357 crossref_primary_10_1016_j_jhydrol_2024_132269 crossref_primary_10_3390_app15020499 crossref_primary_10_1016_j_mlwa_2025_100706 crossref_primary_10_1016_j_jhydrol_2025_133594 crossref_primary_10_3390_polym16233368 crossref_primary_10_1016_j_ifacsc_2025_100298 crossref_primary_10_3390_earth6030069 crossref_primary_10_5194_hess_28_4407_2024 crossref_primary_10_1016_j_jhydrol_2025_133111 crossref_primary_10_3390_w17152341 crossref_primary_10_3390_w17182722 crossref_primary_10_1098_rsta_2024_0287 crossref_primary_10_3390_w17030339 crossref_primary_10_5194_hess_29_1061_2025 crossref_primary_10_1016_j_ejrh_2025_102719 crossref_primary_10_3390_app15126690 crossref_primary_10_5194_hess_29_2521_2025 crossref_primary_10_1016_j_jhydrol_2025_133689 crossref_primary_10_5194_hess_29_1749_2025 crossref_primary_10_1016_j_jhydrol_2025_133764 crossref_primary_10_1007_s11269_025_04231_5 crossref_primary_10_1007_s40808_025_02316_z crossref_primary_10_1073_pnas_2503160122 crossref_primary_10_5194_hess_29_3405_2025 crossref_primary_10_5194_hess_28_4187_2024 crossref_primary_10_1038_s41598_025_99838_4 crossref_primary_10_1016_j_jhydrol_2024_132471 crossref_primary_10_5194_hess_29_4515_2025 crossref_primary_10_1016_j_jhydrol_2025_134270 crossref_primary_10_5194_hess_29_1277_2025 |
| Cites_doi | 10.1002/2015WR018247 10.5194/hess-21-3953-2017 10.5194/hess-19-209-2015 10.5194/hess-21-5293-2017 10.1002/2017WR020401 10.1029/2022WR033918 10.1016/0022-1694(70)90255-6 10.1038/s41586-024-07145-1 10.5194/hess-23-2601-2019 10.1098/rspl.1895.0041 10.1038/s41597-023-01975-w 10.1029/2019WR026065 10.1029/2019JD030767 10.1029/2008WR007327 10.5194/hess-22-6005-2018 10.5194/hess-25-2045-2021 10.21105/joss.04050 10.1029/2020WR028600 10.1111/1752-1688.12964 10.5194/hess-26-3377-2022 10.5194/hess-28-4187-2024 10.1016/j.jhydrol.2009.08.003 10.5194/hess-26-5493-2022 10.5194/hess-23-5089-2019 10.31223/X57090 10.1002/wat2.1487 10.4211/hs.474ecc37e7db45baa425cdb4fc1b61e1 10.1029/2020WR028091 10.1175/JHM-D-16-0284.1 10.1029/2011WR011044 10.5194/hess-26-1673-2022 10.5194/hess-25-2685-2021 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2024 Copernicus GmbH 2024. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2024 Copernicus GmbH – notice: 2024. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION ISR 7QH 7TG 7UA 8FD 8FE 8FG ABJCF ABUWG AEUYN AFKRA ATCPS AZQEC BENPR BFMQW BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F1W FR3 GNUQQ H96 HCIFZ KL. KR7 L.G L6V M7S PATMY PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS PYCSY DOA |
| DOI | 10.5194/hess-28-4187-2024 |
| DatabaseName | CrossRef Gale In Context: Science Aqualine Meteorological & Geoastrophysical Abstracts Water Resources Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Agricultural & Environmental Science Collection ProQuest Central Essentials - QC ProQuest Central Continental Europe Database Technology Collection Natural Science Collection ProQuest Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database ProQuest Central Student Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources SciTech Premium Collection Meteorological & Geoastrophysical Abstracts - Academic Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional ProQuest Engineering Collection Engineering Database Environmental Science Database Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering collection Environmental Science Collection DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database Aquatic Science & Fisheries Abstracts (ASFA) Professional ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China Water Resources Abstracts Environmental Sciences and Pollution Management Earth, Atmospheric & Aquatic Science Collection ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Engineering Collection Meteorological & Geoastrophysical Abstracts Natural Science Collection ProQuest Central Korea Agricultural & Environmental Science Collection ProQuest Central (New) Engineering Collection Civil Engineering Abstracts Engineering Database ProQuest One Academic Eastern Edition Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Continental Europe Database ProQuest SciTech Collection Aqualine Environmental Science Collection Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources ProQuest One Academic UKI Edition ASFA: Aquatic Sciences and Fisheries Abstracts Materials Science & Engineering Collection Environmental Science Database Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geography |
| EISSN | 1607-7938 |
| EndPage | 4201 |
| ExternalDocumentID | oai_doaj_org_article_1390265d747745d3be4d827ff8e2ee92 A808508124 10_5194_hess_28_4187_2024 |
| GroupedDBID | 29I 2WC 5GY 5VS 7XC 8CJ 8FE 8FG 8FH 8R4 8R5 AAFWJ AAYXX ABJCF ABUWG ACGFO ACIWK ADBBV AENEX AEUYN AFFHD AFKRA AFPKN AFRAH AHGZY AIAGR ALMA_UNASSIGNED_HOLDINGS ATCPS BANNL BCNDV BENPR BFMQW BGLVJ BHPHI BKSAR BPHCQ CCPQU CITATION D1J D1K E3Z EBS ECGQY EDH EJD GROUPED_DOAJ GX1 H13 HCIFZ IAO IEA IEP IGS ISR ITC K6- KQ8 L6V L8X LK5 M7R M7S OK1 OVT P2P PATMY PCBAR PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC PTHSS PYCSY Q2X RKB RNS TR2 XSB ~02 ~KM 7QH 7TG 7UA 8FD AZQEC C1K DWQXO F1W FR3 GNUQQ H96 KL. KR7 L.G PKEHL PQEST PQUKI PRINS |
| ID | FETCH-LOGICAL-c483t-4221c8ad930e5c2488a8cf3f523c0d8057db39043ac642961c811a30b32b9f13 |
| IEDL.DBID | RKB |
| ISICitedReferencesCount | 59 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001310057400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1607-7938 1027-5606 |
| IngestDate | Tue Oct 14 19:08:40 EDT 2025 Fri Jul 25 12:23:28 EDT 2025 Mon Nov 24 16:09:32 EST 2025 Mon Nov 24 15:42:31 EST 2025 Wed Nov 26 11:09:58 EST 2025 Tue Nov 18 21:57:54 EST 2025 Sat Nov 29 05:01:59 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 17 |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c483t-4221c8ad930e5c2488a8cf3f523c0d8057db39043ac642961c811a30b32b9f13 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-9843-6798 0000-0002-8897-7689 0000-0002-4587-898X |
| OpenAccessLink | https://doaj.org/article/1390265d747745d3be4d827ff8e2ee92 |
| PQID | 3103072073 |
| PQPubID | 105724 |
| PageCount | 15 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_1390265d747745d3be4d827ff8e2ee92 proquest_journals_3103072073 gale_infotracmisc_A808508124 gale_infotracacademiconefile_A808508124 gale_incontextgauss_ISR_A808508124 crossref_citationtrail_10_5194_hess_28_4187_2024 crossref_primary_10_5194_hess_28_4187_2024 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-09-12 |
| PublicationDateYYYYMMDD | 2024-09-12 |
| PublicationDate_xml | – month: 09 year: 2024 text: 2024-09-12 day: 12 |
| PublicationDecade | 2020 |
| PublicationPlace | Katlenburg-Lindau |
| PublicationPlace_xml | – name: Katlenburg-Lindau |
| PublicationTitle | Hydrology and earth system sciences |
| PublicationYear | 2024 |
| Publisher | Copernicus GmbH Copernicus Publications |
| Publisher_xml | – name: Copernicus GmbH – name: Copernicus Publications |
| References | ref13 ref35 ref12 ref34 ref15 ref14 ref31 ref30 ref11 ref33 ref10 ref32 ref2 ref1 ref17 ref16 ref19 ref18 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 |
| References_xml | – ident: ref3 doi: 10.1002/2015WR018247 – ident: ref11 doi: 10.5194/hess-21-3953-2017 – ident: ref30 doi: 10.5194/hess-19-209-2015 – ident: ref1 doi: 10.5194/hess-21-5293-2017 – ident: ref23 doi: 10.1002/2017WR020401 – ident: ref7 doi: 10.1029/2022WR033918 – ident: ref25 doi: 10.1016/0022-1694(70)90255-6 – ident: ref26 doi: 10.1038/s41586-024-07145-1 – ident: ref24 doi: 10.5194/hess-23-2601-2019 – ident: ref32 doi: 10.1098/rspl.1895.0041 – ident: ref21 doi: 10.1038/s41597-023-01975-w – ident: ref27 – ident: ref17 doi: 10.1029/2019WR026065 – ident: ref33 doi: 10.1029/2019JD030767 – ident: ref34 doi: 10.1029/2008WR007327 – ident: ref16 doi: 10.5194/hess-22-6005-2018 – ident: ref6 doi: 10.5194/hess-25-2045-2021 – ident: ref20 doi: 10.21105/joss.04050 – ident: ref22 doi: 10.1029/2020WR028600 – ident: ref4 doi: 10.1111/1752-1688.12964 – ident: ref2 – ident: ref5 doi: 10.5194/hess-26-3377-2022 – ident: ref15 doi: 10.5194/hess-28-4187-2024 – ident: ref9 doi: 10.1016/j.jhydrol.2009.08.003 – ident: ref29 doi: 10.5194/hess-26-5493-2022 – ident: ref18 doi: 10.5194/hess-23-5089-2019 – ident: ref14 doi: 10.31223/X57090 – ident: ref8 doi: 10.1002/wat2.1487 – ident: ref13 doi: 10.4211/hs.474ecc37e7db45baa425cdb4fc1b61e1 – ident: ref28 doi: 10.1029/2020WR028091 – ident: ref31 doi: 10.1175/JHM-D-16-0284.1 – ident: ref10 doi: 10.1029/2011WR011044 – ident: ref12 doi: 10.5194/hess-26-1673-2022 – ident: ref19 doi: 10.5194/hess-25-2685-2021 – ident: ref35 |
| SSID | ssj0028862 |
| Score | 2.6135397 |
| Snippet | Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for... Machine learning (ML) has played an increasing role in the hydrological sciences. In particular, Long Short-Term Memory (LSTM) networks are popular for... |
| SourceID | doaj proquest gale crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 4187 |
| SubjectTerms | Basins Best practice Best practices Camelids Datasets Deep learning Hydrologic models Hydrology Long short-term memory Machine learning Precipitation Rainfall-runoff modeling Rainfall-runoff relationships Runoff models Stream flow Watersheds |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwELYqhFQuqKVFLKXIQpVKK1nEj2wcboBAVNpdqm4O3CzHsdmVUHa1DyT-PTOJF3UPwIVrMnn4y9jzje18Q8gPLrMqtbxkqEXHVM4dsxBnGZBnDblbN6lisYlsMNC3t_nf_0p94Z6wVh64Be4EGAqkCWkFtDdTaSVLryotshC0F97nzeibZPkqmYqpltbddp1TZAxierddzwS2ok5GMIIwnFLi0L0g9VdrEakR7n9peG5iztUnsh3JIj1rX_Iz-eDrHfIx1i0fPX4hxTXgR2-m4xq955QOPHgmbco-UEt7k_qODkdAsFkBAzDt467aR3rcGxb9X7RuN4DTCZrijMG9pxDTxvVXUlxdFhfXLNZJYE5puWBKCO60rXKZ-NQJ6JJWuyAD5JguqTQwsqoEFJW0DrKNvAvGnFuZlFKUeeByl2zUk9rvESrholD6IFMPPEloi1UxlCq5D3DrLHRIsoLKuKghjm26N5BLILoG0TVCG0TXILod8vv5kmkroPGa8Tni_2yI2tfNAfAIEz3CvOURHXKEX8-gukWN22fu7BKe82f4z5xpVOhDTtMhP6NRmEALnI1_IwAOKIi1ZnmwZgndz62fXjmJid1_bpribRn4pdx_jxZ9I1uIDmsqVxyQjcVs6b-TTfewGM9nh43nPwGtA__J priority: 102 providerName: Directory of Open Access Journals – databaseName: Engineering Database dbid: M7S link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Jb9NAFB5BQYJLyypSWjRCSCzSqJ7F8ZhLVRBVkdqAiA-9jcazJJEqOyQpUv8979mTVDnQC9f4Ocn47TPP30fIOy4Ln1teM8SiY6rkjlnIswyKZw292zDziWyiGI305WX5M224LdNY5TomdoHatw73yI86PqxCgEUez38zZI3C09VEoXGfPECUBN6N7o03DZfWw_60UxQMMvuwP9WEmkUdTSGOMNxY4uBkIhNqKy918P3_CtJd5jnd-9___ITsppqTnvRG8pTcC80z8ijRn09vnpPqDNRAf8xnDRrhZzoKYOC0Y4-glp63zYSOp1CnswriOL3A4dwb-uF8XF18pE0_R05bFMWNh6tAITXOmhekOv1WfT1jiW6BOaXliikhuNPWlzILuRPg2Va7KCO0qi7zGgo7X8syU9I6aFrKIQhzbmVWS1GXkcuXZKdpm_CKUAk3xTpEmQcot4S2SK6hVM1DhK8u4oBk62dtXIIixzVdGWhJUD0G1WOENqgeg-oZkE-bW-Y9Dsddwl9QgRtBhNDuPmgXE5M80kDpC_1n7qGfKlTuZR2U16KIUQcRQikG5C2q3yBIRoNTOBN7Db_zffzLnGgE-sPSaEDeJ6HYwgqcTS81wHNAXK0tyYMtSfBit315bUImRZGlubWf_bsvvyaPcd2so7Y4IDurxXU4JA_dn9VsuXjTOcVfo4AOOw priority: 102 providerName: ProQuest |
| Title | HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin |
| URI | https://www.proquest.com/docview/3103072073 https://doaj.org/article/1390265d747745d3be4d827ff8e2ee92 |
| Volume | 28 |
| WOSCitedRecordID | wos001310057400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAGF databaseName: Copernicus Publications customDbUrl: eissn: 1607-7938 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0028862 issn: 1607-7938 databaseCode: RKB dateStart: 19970101 isFulltext: true titleUrlDefault: http://publications.copernicus.org/open-access_journals/open_access_journals_a_z.html providerName: Copernicus Gesellschaft – providerCode: PRVAON databaseName: Directory of Open Access Journals customDbUrl: eissn: 1607-7938 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0028862 issn: 1607-7938 databaseCode: DOA dateStart: 19970101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: Continental Europe Database customDbUrl: eissn: 1607-7938 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0028862 issn: 1607-7938 databaseCode: BFMQW dateStart: 20090601 isFulltext: true titleUrlDefault: https://search.proquest.com/conteurope providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 1607-7938 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0028862 issn: 1607-7938 databaseCode: PCBAR dateStart: 20090601 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1607-7938 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0028862 issn: 1607-7938 databaseCode: M7S dateStart: 20090601 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database customDbUrl: eissn: 1607-7938 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0028862 issn: 1607-7938 databaseCode: PATMY dateStart: 20090601 isFulltext: true titleUrlDefault: http://search.proquest.com/environmentalscience providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1607-7938 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0028862 issn: 1607-7938 databaseCode: BENPR dateStart: 20090601 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1607-7938 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0028862 issn: 1607-7938 databaseCode: PIMPY dateStart: 20090601 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dT9swELcQQ9pe2NiHKLDKmibBJlnEH2mcvRUEAol2VZsH9mQ5jk0roRRBQeK_311iEH0Ye4CnSMk5ie98X87ld4R85zKrUstLhlh0TOXcMQt-lkHwrCF36yVVbDaRDYf6_DwfPWn1hTVhLTxwy7h9iFAgTUgrCHszlVay9KrSIgtBe-F9jtYXliGq5Bh7uMVUS-te-51TZAx8eq_9ngnRitqfggVhuKXEQb0g9VdLHqkB7v-XeW58zvH7F7ztB7IeA03ab4dskBVffyRvY8_z6f0nUpwA7-nvq1mNK-8XHXpY1bRpGUEtPZvXF3QyheCcFWC86QArcu_p3tmkGPygdVs8TudIirsNl56CP5zVn0lxfFQcnrDYY4E5peWCKSG407bKZeJTJ0CdrXZBBshPXVJpiOaqEuakpHWQqeQ9IObcyqSUoswDl1_Iaj2v_SahEgaF0geZeoixhLbYUUOpkvsAt85ChyQPbDYu4o_jnC4N5CEoGYOSMUIblIxByXTIz8chVy34xnPEByiOR0LEzW5OgHxMlI_5n3w65BtK3iAyRo2lNxf2Fp5zOhmbvkZ0P4yHOmQ3EoU5zMDZ-CcD8AHBtJYod5YoQXXd8uWHBWai6bgxTeO3TIDp3XqNGW2Td8gd1nS92CGri-tb_5WsubvF7Oa6S94cHA1H426zFdHFwtcJnBv1i8EfPJ4ORnBEzfoLa64YqQ |
| linkProvider | Copernicus Gesellschaft |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lj9MwEB4tXaTlwhtRWMBCIB6StYntNg4SQstj1WrbUtEclpOVOE5baZWUtgvqj-I_MpNHUQ_sbQ9cm0kqO9_MfGM78wG88GWQdmI_4dSLjqvQtzzGPMuRPGus3bpeWotNBKORPjsLx3vwu_kWho5VNjGxDNRpYWmN_KjUwwoEIvLD4gcn1SjaXW0kNCpYnLrNLyzZVu_7n_H9vhTi5Ev0qcdrVQFulZZrroTwrY7TUHquYwUCONY2kxlWZNZLNfKXNJGhp2RskZuHXTT2_Vh6iRRJmPkSH3sN9hVhvQX74_5w_H1b4WndrbZXRcCRSnSrbVQkSepohoGL00qWj14tPKF2EmGpF_CvrFCmupNb_9kk3YabNadmx5UT3IE9l9-Fg1refba5B1EPYca-LuY5Odk7NnLowKxUx2AxGxT5lE1mWIfwCPMUG9Lh4w17PZhEwzcsr87Js4JMaWHl3DFM_fP8PkRXMaYH0MqL3D0EJvGmLHGZ7Dikk0LHJB6iVOK7DB8dZG3wmldrbN1qncZ0brDkIjQYQoMR2hAaDKGhDW-3tyyqPiOXGX8kvGwNqUV4-UOxnJo64hik9lhfd1KsFwPVSWXiVKpFkGXaCedC0YbnhDZDTUByOmU0jS_wf_qTb-ZYUyNDon5teFUbZQWOwMb1Rxs4D9Q3bMfycMcSo5Tdvdwg1tRRcmX-wvXR5ZefwUEvGg7MoD86fQw3aA54KeNxCK318sI9gev253q-Wj6tPZKBuWJ4_wG9YWk8 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3fb9MwELZGh4AXfiMKAywE4odkNbHTxEFCaGNUq9aWsUZib1bi2G2lKSltB-qfxn_HXeIU9YG97YHX5pLKznd339nOfYS88kWUd1M_Y9iLjgWxr1kKeZYBeZZQu4Ve7sQmotFInp3FJzvkd_MtDB6rbGJiFajzUuMaeafSw4o4ILJj3bGIk8Pep_kPhgpSuNPayGnUEDk2619Qvi0_9g_hXb_mvPcl-XzEnMIA04EUKxZw7muZ5rHwTFdzAHMqtRUWqjPt5RK4TJ6J2AtEqoGnxyEY-34qvEzwLLa-gMdeI7syDKXXIrsHveG375tqT8qw3mrlEQNaEdZbqkCYgs4UghjDVS0fPJx7PNhKipV2wL8yRJX2enf-4wm7S247rk33a-e4R3ZMcZ_cdLLv0_UDkhwB_OjX-axA5_tARwYcm1aqGTSlg7KY0PEU6hOWQP6iQzyUvKZvB-Nk-I4W9fl5WqIpLricGwqUYFY8JMlVjOkRaRVlYR4TKuAmmxkrugZoJpcpiooEQeYbC4-ObJt4zWtW2rVgxzGdKyjFEBkKkaG4VIgMhchok_ebW-Z1_5HLjA8QOxtDbB1e_VAuJspFIgWUH-rubg51ZBR0c5GZIJc8slYabkzM2-QlIk9hc5ACUTNJL-B_-uNTtS-xwSFSwjZ544xsCSPQqfuYA-YB-4ltWe5tWUL00tuXG_QqFz2X6i90n1x--QW5AZhWg_7o-Cm5hVPAKnWPPdJaLS7MM3Jd_1zNlovnzjkpUVeM7j-ib3Hc |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=HESS+Opinions%3A+Never+train+a+Long+Short-Term+Memory+%28LSTM%29+network+on+a+single+basin&rft.jtitle=Hydrology+and+earth+system+sciences&rft.au=F.+Kratzert&rft.au=M.+Gauch&rft.au=D.+Klotz&rft.au=G.+Nearing&rft.date=2024-09-12&rft.pub=Copernicus+Publications&rft.issn=1027-5606&rft.eissn=1607-7938&rft.volume=28&rft.spage=4187&rft.epage=4201&rft_id=info:doi/10.5194%2Fhess-28-4187-2024&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_1390265d747745d3be4d827ff8e2ee92 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1607-7938&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1607-7938&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1607-7938&client=summon |