首页|Study Findings on Machine Learning Detailed by Researchers atFederal University of Juiz de Fora (Long-term natural streamflowforecasting under drought scenari os using data-intelligence modeling)
Study Findings on Machine Learning Detailed by Researchers atFederal University of Juiz de Fora (Long-term natural streamflowforecasting under drought scenari os using data-intelligence modeling)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on artificial intelligenc e is the subject of a new report. According to newsreporting originating from J uiz de Fora, Brazil, by NewsRx correspondents, research stated, “Long-termriver streamflow prediction and modeling are essential for water resource management and decision-makingrelated to water resources.”The news journalists obtained a quote from the research from Federal University of Juiz de Fora: “Thisresearch paper considers the importance of these predicti ons and proposes a model to address scarcityscenarios to support decision-makin g in water allocation, flood management, and drought prediction scenarios.Machi ne learning (ML) techniques offer promising alternatives for improving long-term streamflowprediction. However, most existing studies on ML models for streamfl ow prediction have focused onshorter time horizons, limiting their broader appl icability. Consequently, there is a need for dedicatedresearch that addresses t he use of ML models in long-term streamflow prediction. Considering this research gap, this paper presents an ML-based approach that learns and replicates the n atural flow dynamics ofa river, allowing for the simulation of reduced flow sce narios (25 % and 50 % reduction). This capabilityal lows for simulating drought scenarios of varying severity, providing valuable in sights for water servicemanagers.”
Federal University of Juiz de ForaJuiz de ForaBrazilSouth AmericaCyborgsEmerging TechnologiesMachine Learnin g
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