首页|University of Wisconsin Madison Researcher Reports on Findings in Machine Learni ng (Improving Subseasonal Soil Moisture and Evaporative Stress Index Forecasts t hrough Machine Learning: The Role of Initial Land State versus Dynamical Model O utput)
University of Wisconsin Madison Researcher Reports on Findings in Machine Learni ng (Improving Subseasonal Soil Moisture and Evaporative Stress Index Forecasts t hrough Machine Learning: The Role of Initial Land State versus Dynamical Model O utput)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news reporting from Madison, Wisconsin, by NewsRx journalists, research stated, "The effect of machine learni ng and other enhancements on statistical-dynamical forecasts of soil moisture (0 -10cm and 0-100cm) and a reference evapotranspiration fraction (Evaporative Stre ss Index, ESI) on sub-seasonal time scales (15-28 days) are explored." The news journalists obtained a quote from the research from University of Wisco nsin Madison: "The predictors include the current and past land surface conditio ns, and dynamical model hindcasts from the Sub-seasonal to Seasonal (S2S) Predic tion Project. When the methods are enhanced with machine learning and other impr ovements, the increases in skill are almost exclusively coming from predictors d rawn from observations of current and past land surface states. This suggests th at operational S2S flash drought forecasts should focus on optimizing use of inf ormation on current conditions rather than on integrating dynamically based fore casts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Im provements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors."
University of Wisconsin MadisonMadisonWisconsinUnited StatesNorth and Central AmericaCyborgsEmerging Technol ogiesMachine Learning
NETL
NSTL
