Please use this identifier to cite or link to this item:
http://hdl.handle.net/2122/16148
DC Field | Value | Language |
---|---|---|
dc.date.accessioned | 2023-02-09T11:35:14Z | - |
dc.date.available | 2023-02-09T11:35:14Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://hdl.handle.net/2122/16148 | - |
dc.description.abstract | By leveraging monitoring data for the Gran Sasso carbonate aquifer during two significant seismic sequences that hit central Italy in recent years, this study investigates the possibility of using memory-enabled deep learning algorithms as meaningful tools for an enhanced modelling of the hydrological response of karst aquifers subject to earthquake phenomena. Meteorological, hydrological and seismic data are used to train and validate long short-term memory networks (LSTM) in one- and multiple-day ahead flow forecasting exercises, aimed at assessing model sensitivities to input variables and modelling choices (training data and parameters of the models). Results indicate that the models fairly reproduce the flow patterns for the considered spring in the Gran Sasso aquifer, thus supporting the potential use of these models for hydrological applications in similar areas, provided that sufficient data are available for the training of the network. | en_US |
dc.language.iso | English | en_US |
dc.publisher.name | Elsevier | en_US |
dc.relation.ispartof | Journal of Hydrology | en_US |
dc.relation.ispartofseries | /617 (2023) | en_US |
dc.rights | CC0 1.0 Universal | * |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
dc.subject | Earthquake hydrology | en_US |
dc.subject | Seismic sequences | en_US |
dc.subject | Karst aquifer | en_US |
dc.subject | Deep learning LSTM | en_US |
dc.subject | Central Italy | en_US |
dc.subject | LSTM | en_US |
dc.title | Deep learning for earthquake hydrology? Insights from the karst Gran Sasso aquifer in central Italy | en_US |
dc.type | article | en |
dc.description.status | Published | en_US |
dc.description.pagenumber | 129002 | en_US |
dc.identifier.doi | 10.1016/j.jhydrol.2022.129002 | en_US |
dc.description.obiettivoSpecifico | 9T. Geochimica dei fluidi applicata allo studio e al monitoraggio di aree sismiche | en_US |
dc.description.journalType | JCR Journal | en_US |
dc.relation.issn | 0022-1694 | en_US |
dc.contributor.author | Scorzini, Anna Rita | - |
dc.contributor.author | Di Bacco, Mario | - |
dc.contributor.author | De Luca, Gaetano | - |
dc.contributor.author | Tallini, Marco | - |
dc.contributor.department | Dipartimento di Ingegneria Civile, Edile-Architettura e Ambientale, Universit`a degli Studi dell’Aquila, | en_US |
dc.contributor.department | Dipartimento di Ingegneria Civile, Edile-Architettura e Ambientale, Universit`a degli Studi dell’Aquila, | en_US |
dc.contributor.department | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione ONT, Roma, Italia | en_US |
dc.contributor.department | Dipartimento di Ingegneria Civile, Edile-Architettura e Ambientale, Universit`a degli Studi dell’Aquila, | en_US |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.grantfulltext | restricted | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | Dipartimento di Ingegneria Civile, Edile-Architettura e Ambientale, Universit`a degli Studi dell’Aquila, | - |
crisitem.author.dept | Dipartimento di Ingegneria Civile, Edile-Architettura e Ambientale, Universit`a degli Studi dell’Aquila, | - |
crisitem.author.dept | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione ONT, Roma, Italia | - |
crisitem.author.orcid | 0000-0002-5704-8481 | - |
crisitem.author.orcid | 0000-0002-2483-3977 | - |
crisitem.author.orcid | 0000-0002-9482-1795 | - |
crisitem.author.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
crisitem.department.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
Appears in Collections: | Article published / in press |
Files in This Item:
File | Description | Size | Format | Existing users please Login |
---|---|---|---|---|
article.pdf | Restricted Paper | 5.34 MB | Adobe PDF | |
Paper_EH.docx | 1.26 MB | Microsoft Word XML | Embargoed until December 15, 2024 |
Page view(s)
67
checked on Apr 17, 2024
Download(s)
7
checked on Apr 17, 2024