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InTheMED M3.4 Application of the Surrogate Models Under Different Scenarios (Climate and Socio-Economic Changes, Remediation Strategies)
The overall objective of the InTheMED project is to implement innovative and sustainable management tools and remediation strategies for MED aquifers (inland and coastal) in order to mitigate anthropogenic and climate-change threats by creating new long-lasting spaces of social learning among different interdependent stakeholders, NGOs, and scientific researchers in five field case studies. These are located at the two shores of the MED basin, namely in Spain, Greece, Portugal, Tunisia, and Turkey. InTheMED will develop an inclusive process that will establish an ensemble of innovative assessment and management tools and methodologies including a high-resolution monitoring approach, smart modelling, a socio-economic assessment, web-based decision support systems (DSS) and new configurations for governance to validate efficient and sustainable integrated groundwater management in the MED considering both the quantitative and qualitative aspects. This Milestone, namely M3.4, is part of Task 3.4 “Analysis of Different Scenarios” (Lead: UNIPR/ participants: UPV, TUC, IST-ID, CERTE and BU). The aim of Task 3.4 is to simulate different future scenarios considering the impact of climate and socio-economic changes on groundwater resources at the five pilot sites. At this aim, different surrogate models were developed for each case study. The surrogate models will support the implementation of the Fuzzy WebDSS tool (WP6) aimed at assist decision makers in aquifer management. The M3.4 outlines the definition of climate and socio-economic scenarios and reports the application of surrogate models within the context of the selected scenarios.
This project is part of the PRIMA Programme supported by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 1923.
- University of Parma Italy
Machine Learning, Sustainability, Mediterranean, Groundwater, Surrogate model
Machine Learning, Sustainability, Mediterranean, Groundwater, Surrogate model
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