Objective Offshore freshened groundwater (OFG) refers to fluids stored in sediment pores and rock fractures below the seafloor, with a salinity lower than seawater. This phenomenon has been identified globally in continental margins and proposed as a resource that can potentially alleviate water stress in coastal regions. However, the scarcity of data to constrain the distribution and volumes of the reservoirs remains a challenge. OPTIMAL project aims to: (i) develop an interdisciplinary methodology to predict the occurrence and distribution of OFG resources built on Artificial Intelligence and (ii) apply the model globally to infer OFG occurrence and quantify the resource feasibility as a function of distribution characteristics such as offshore extent, depth below the seafloor and fresh to brackish water ratio. The proposed methodology uses a surrogate model to create a dataset of input parameters, representing key geological and geomorphological components influencing OFG systems, such as aquitard thickness and seafloor bathymetry. The output data will be generated via numerical simulation of variable-density groundwater transport on the suite of surrogate models using high-performance computing. These data will be used to train and test machine learning algorithms. The successful models will be validated using real-world data from the existing global OFG database. The predictive model proposed in this fellowship contributes to achieving Sustainable Development Goals related to technologies for improving access to water resources. The primary beneficiary of this funding will be the University of Malta. Partner organizations will be Utrecht University and Deltares in The Netherlands. The action presents a unique opportunity for the fellow to transfer his expertise in stochastic reservoir modelling and characterization of OFG systems to the host, while learning about marine geology, seafloor landforms and applied machine learning. Fields of science natural sciencescomputer and information sciencesdatabasesnatural sciencesearth and related environmental scienceshydrologynatural sciencescomputer and information sciencesartificial intelligencemachine learningnatural sciencesearth and related environmental sciencesgeology Keywords Offshore freshened groundwater High-Performance Computing Machine Learning Geology Geomorphology Programme(s) HORIZON.4.1 - Widening participation and spreading excellence Main Programme HORIZON.4.1.5 - Fostering brain circulation of researchers and excellence initiatives Topic(s) HORIZON-WIDERA-2022-TALENTS-04-01 - Fostering balanced brain circulation – ERA Fellowships Call for proposal HORIZON-WIDERA-2022-TALENTS-04 See other projects for this call Funding Scheme HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships Coordinator UNIVERSITA TA MALTA Net EU contribution € 161 411,52 Address TAL OROQQ 2080 MSIDA Malta See on map Region Malta Malta Malta Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost No data Partners (2) Sort alphabetically Sort by Net EU contribution Expand all Collapse all UNIVERSITEIT UTRECHT Netherlands Net EU contribution € 0,00 Address HEIDELBERGLAAN 8 3584 CS Utrecht See on map Region West-Nederland Utrecht Utrecht Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost No data STICHTING DELTARES Netherlands Net EU contribution € 0,00 Address BOUSSINESQWEG 1 2629 HV Delft See on map Region West-Nederland Zuid-Holland Delft en Westland Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost No data
