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UID:pretalx-flowpath-2025-F3H7KL@talks.staging.osgeo.org
DTSTART;TZID=CET:20250611T100000
DTEND;TZID=CET:20250611T101000
DESCRIPTION:The Emilia-Romagna region (Italy) hosts extensive agricultural 
 and industrial activity\, and densely populated urban areas. Groundwater s
 erves as a crucial freshwater source\, particularly during droughts\, whic
 h are expected to become more frequent and intense.\nThis study estimates 
 the evolution of groundwater conditions in part of Emilia-Romagna\, consid
 ering climate change and human impacts\, to assess the resilience of the r
 egional multi-layered aquifer system to droughts and outline potential gui
 delines for long-term sustainable groundwater management. A numerical grou
 ndwater flow model and a random forest algorithm\, implemented in MODFLOW 
 6 and R respectively\, are applied to compare the performance of a physics
 -based and a machine learning method in simulating past and future groundw
 ater levels\, and to explore the benefits of their combination. Input data
  are sourced from the regional groundwater model by Arpae (Regional Agency
  for Prevention\, Environment and Energy of Emilia-Romagna) and publicly a
 vailable datasets on the Emilia-Romagna Region and Arpae repositories.\nBo
 th techniques are then used to analyze scenarios of reduced precipitation 
 and altered pumping\, focusing on their combined effects on the aquifer sy
 stem. Results show the aquifer system’s vulnerability to future droughts
 . Increased pumping amplifies precipitation reduction effects\, while lowe
 r abstraction partly mitigates them. Critical hotspots are identified\, em
 phasizing the need for multi-scale approaches to develop effective mitigat
 ion and adaptation strategies.\nThe random forest algorithm provides insig
 hts into factors influencing groundwater head distribution\, enhancing the
  groundwater model results interpretation and potential improvement. Howev
 er\, its lack of physical grounding limits its generalization potential. T
 hese findings highlight the value of integrating physics-based and machine
  learning methods to improve their performance\, making a significant cont
 ribution to groundwater modeling.
DTSTAMP:20260428T165433Z
LOCATION:Room R3
SUMMARY:A comparative study of physics-based and machine learning approache
 s for sustainable groundwater management in the Emilia-Romagna region (Ita
 ly) - Ilaria Delfini
URL:https://talks.staging.osgeo.org/flowpath-2025/talk/F3H7KL/
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