首页|New Machine Learning Findings from University of Oklahoma Reported [Adapting Subseasonal-to-seasonal (S2s) Precipitation Forecast At Watersheds for Hydrologic Ensemble Streamflow Forecasting With a Machine Learning-based Post-pr ocessing Approach]
New Machine Learning Findings from University of Oklahoma Reported [Adapting Subseasonal-to-seasonal (S2s) Precipitation Forecast At Watersheds for Hydrologic Ensemble Streamflow Forecasting With a Machine Learning-based Post-pr ocessing Approach]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ma chine Learning. According to news reportingoriginating in Norman, Oklahoma, by NewsRx journalists, research stated, “Accurate and reliable precipitationpredic tions made by dynamical forecast models could provide crucial information for hu mansocioeconomic activities by enabling hydrologic forecasts at the Subseasonal -to-Seasonal (S2S) timescale.To utilize available S2S precipitation predictions for hydrologic forecasts, post-processing techniques havebeen applied to adapt the raw S2S precipitation to local watersheds.”
NormanOklahomaUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Oklahoma
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