首页|Reports from Oak Ridge National Laboratory Add New Data to Findings in Machine Learning (Advancing Subseasonal Reservoir Inflow Forecasts Using an Explainable Machine Learning Method)
Reports from Oak Ridge National Laboratory Add New Data to Findings in Machine Learning (Advancing Subseasonal Reservoir Inflow Forecasts Using an Explainable Machine Learning Method)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Current study results on Machine Learning have been published. According to newsreporting from Oak Ridge, Tennessee, by NewsRx journalists, research stated, “Region Upper ColoradoRiver Basin and Great Basin in the United StatesStudy Focus Accurate subseasonal reservoir inflow forecastsand understanding the influence of hydrometeorological forcings on these forecasts are crucial for improvingwater resources management. Machine learning (ML) techniques, such as long short-term memory (LSTM)networks, perform well for short-term inflow forecasts but have deficiencies in subseasonal forecasts andlack interpretability.”
Oak RidgeTennesseeUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningOak Ridge National Laboratory
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