首页|Studies from Iowa State University Further Understanding of Machine Learning (A Machine Learning Approach To Improve the Usability of Severe Thunderstorm Wind R eports)
Studies from Iowa State University Further Understanding of Machine Learning (A Machine Learning Approach To Improve the Usability of Severe Thunderstorm Wind R eports)
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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 reportingfrom Ames, Iowa, by NewsRx journalis ts, research stated, “Many concerns are known to exist withthunderstorm wind re ports in the National speed, changes in report frequency due to population densi ty,and differences in reporting due to damage tracers. These concerns are espec ially pronounced with reportsthat are not associated with a wind speed measurem ent, but are estimated, which make up almost 90%of the database.”Funders for this research include HWT Program within the NOAA/OAR Weather Progra m Office,Department of Commerce, HPC@ISU equipment at Iowa State University, Na tional Science Foundation(NSF).The news correspondents obtained a quote from the research from Iowa State Unive rsity, “We haveused machine learning to predict the probability that a severe w ind report was caused by severe intensitywind, or wind > = 50 kt (similar to 25 m s-1). A total of six machine learning models were train ed on 11years of measured thunderstorm wind reports, along with meteorological parameters, population density,and elevation. Objective skill metrics such as t he area under the ROC curve (AUC), Brier score, andreliability curves suggest t hat the best performing model is the stacked generalized linear model, whichhas an AUC around 0.9 and a Brier score around 0.1. The outputs from these models h ave many potentialuses such as forecast verification and quality control for im plementation in forecast tools.”
AmesIowaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningIowa State University
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