Publication Date:
2023-02-09
Description:
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.
Description:
Published
Description:
129002
Description:
9T. Geochimica dei fluidi applicata allo studio e al monitoraggio di aree sismiche
Description:
JCR Journal
Keywords:
Earthquake hydrology
;
Seismic sequences
;
Karst aquifer
;
Deep learning LSTM
;
Central Italy
;
LSTM
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
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