Please use this identifier to cite or link to this item:
http://hdl.handle.net/2122/16261
DC Field | Value | Language |
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dc.date.accessioned | 2023-02-28T07:41:17Z | - |
dc.date.available | 2023-02-28T07:41:17Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://hdl.handle.net/2122/16261 | - |
dc.description.abstract | In 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.iso | English | en_US |
dc.publisher.name | Springer | en_US |
dc.relation.ispartof | Earth Science Informatics | en_US |
dc.relation.ispartofseries | /16 (2023) | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.title | CleverRiver: an open source and free Google Colab toolkit for deep-learning river-flow models | en_US |
dc.type | article | en |
dc.description.status | Published | en_US |
dc.type.QualityControl | Peer-reviewed | en_US |
dc.description.pagenumber | 1119–1130 | en_US |
dc.identifier.doi | 10.1007/s12145-022-00903-7 | en_US |
dc.description.obiettivoSpecifico | 3IT. Calcolo scientifico | en_US |
dc.description.journalType | JCR Journal | en_US |
dc.contributor.author | Luppichini, Marco | - |
dc.contributor.author | Bini, Monica | - |
dc.contributor.author | Giannecchini, Roberto | - |
dc.contributor.department | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Pisa, Pisa, Italia | en_US |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | Università di Pisa | - |
crisitem.author.dept | Dipartimento di Scienze della Terra Università di Pisa | - |
crisitem.author.orcid | 0000-0003-1482-2630 | - |
crisitem.author.orcid | 0000-0003-0447-3086 | - |
crisitem.department.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
Appears in Collections: | Article published / in press |
Files in This Item:
File | Description | Size | Format | |
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Luppichini_et_al-2022.pdf | Open Access Published Paper | 2.66 MB | Adobe PDF | View/Open |
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