Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/16261
DC FieldValueLanguage
dc.date.accessioned2023-02-28T07:41:17Z-
dc.date.available2023-02-28T07:41:17Z-
dc.date.issued2023-
dc.identifier.urihttp://hdl.handle.net/2122/16261-
dc.description.abstractIn a period in which climate change is signifcantly varying rainfall regimes and their intensity all over the world, river-fow prediction is a major concern of geosciences. In recent years there has been an increase in the use of deep-learning models for river-fow prediction. However, in this feld we can observe two main issues: i) many case studies use similar (or the same) strategies without sharing the codes, and ii) the application of these techniques requires good computer knowledge. This work proposes to employ a Google Colab notebook called CleverRiver, which allows the application of deep-learning for river-fow predictions. CleverRiver is a dynamic software that can be upgraded and modifed not only by the authors but also by the users. The main advantages of CleverRiver are the following: the software is not limited by the client hardware, operating systems, etc.; the code is open-source; the toolkit is integrated with user-friendly interfaces; updated releases with new architectures, data management, and model parameters will be progressively uploaded. The software consists of three sections: the frst one enables to train the models by means of some architectures, parameters, and data; the second section allows to create predictions by using the trained models; the third section allows to send feedback and to share experiences with the authors, providing a fux of precious information able to improve scientifc research.en_US
dc.language.isoEnglishen_US
dc.publisher.nameSpringeren_US
dc.relation.ispartofEarth Science Informaticsen_US
dc.relation.ispartofseries/16 (2023)en_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.titleCleverRiver: an open source and free Google Colab toolkit for deep-learning river-flow modelsen_US
dc.typearticleen
dc.description.statusPublisheden_US
dc.type.QualityControlPeer-revieweden_US
dc.description.pagenumber1119–1130en_US
dc.identifier.doi10.1007/s12145-022-00903-7en_US
dc.description.obiettivoSpecifico3IT. Calcolo scientificoen_US
dc.description.journalTypeJCR Journalen_US
dc.contributor.authorLuppichini, Marco-
dc.contributor.authorBini, Monica-
dc.contributor.authorGiannecchini, Roberto-
dc.contributor.departmentIstituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Pisa, Pisa, Italiaen_US
item.openairetypearticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
crisitem.author.deptUniversità di Pisa-
crisitem.author.deptDipartimento di Scienze della Terra Università di Pisa-
crisitem.author.orcid0000-0003-1482-2630-
crisitem.author.orcid0000-0003-0447-3086-
crisitem.department.parentorgIstituto Nazionale di Geofisica e Vulcanologia-
Appears in Collections:Article published / in press
Files in This Item:
File Description SizeFormat
Luppichini_et_al-2022.pdfOpen Access Published Paper2.66 MBAdobe PDFView/Open
Show simple item record

Page view(s)

33
checked on Apr 20, 2024

Download(s)

15
checked on Apr 20, 2024

Google ScholarTM

Check

Altmetric