Please use this identifier to cite or link to this item:
Authors: Scorzini, Anna Rita* 
Di Bacco, Mario* 
De Luca, Gaetano* 
Tallini, Marco* 
Title: Deep learning for earthquake hydrology? Insights from the karst Gran Sasso aquifer in central Italy
Journal: Journal of Hydrology 
Series/Report no.: /617 (2023)
Publisher: Elsevier
Issue Date: 2023
DOI: 10.1016/j.jhydrol.2022.129002
Keywords: Earthquake hydrology
Seismic sequences
Karst aquifer
Deep learning LSTM
Central Italy
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.
Appears in Collections:Article published / in press

Files in This Item:
File Description SizeFormat Existing users please Login
article.pdfRestricted Paper5.34 MBAdobe PDF
Paper_EH.docx1.26 MBMicrosoft Word XMLEmbargoed until December 15, 2024
Show full item record

Page view(s)

checked on Mar 31, 2023


checked on Mar 31, 2023

Google ScholarTM