首页|New Findings from Swiss Federal Institute of Aquatic Science and Technology (EAW AG) in the Area of Machine Learning Reported (Groundwater Vulnerability To Pollu tion In Africa's Sahel Region)
New Findings from Swiss Federal Institute of Aquatic Science and Technology (EAW AG) in the Area of Machine Learning Reported (Groundwater Vulnerability To Pollu tion In Africa's Sahel Region)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news originating from Dubendorf, Switzerland, by Ne wsRx correspondents, research stated, "Protection of groundwater resources is es sential to ensure quality and sustainable use. However, predicting vulnerability to anthropogenic pollution can be difficult where data are limited." Funders for this research include Swiss Federal Office of Energy (SFOE), Norwegi an Agency for Development Cooperation - NORAD. Our news journalists obtained a quote from the research from the Swiss Federal I nstitute of Aquatic Science and Technology (EAWAG), "This is particularly true i n the Sahel region of Africa, which has a rapidly growing population and increas ing water demands. Here we use groundwater measurements of tritium (3H) with mac hine learning to create an aquifer vulnerability map (of the western Sahel), whi ch forms an important basis for sustainable groundwater management. Modelling sh ows that arid areas with greater precipitation seasonality, higher permeability and deeper wells or water table generally have older groundwater and less vulner ability to pollution. About half of the modelled area was classified as vulnerab le. Groundwater vulnerability is based on recent recharge, implying a sensitivit y also to a changing climate, for example, through altered precipitation or evap otranspiration. This study showcases the efficacy of using tritium to assess aqu ifer vulnerability and the value of tritium analyses in groundwater, particularl y towards improving the spatial and temporal resolution. Assessing the resilienc e of groundwater resources can be challenging in data-sparse regions."
DubendorfSwitzerlandEuropeCyborgsEmerging TechnologiesMachine LearningRadioisotopesTritiumSwiss Federal Institute of Aquatic Science and Technology (EAWAG)
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