首页|Recent Findings from Federal University Rio de Janeiro Provides New Insights into Machine Learning (Application of Machine Learning Models for Convective Meteorological Events)
Recent Findings from Federal University Rio de Janeiro Provides New Insights into Machine Learning (Application of Machine Learning Models for Convective Meteorological Events)
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Springer Nature
Current study results on Machine Learning have been published. According to news reporting from Rio de Janeiro, Brazil, by NewsRx journalists, research stated, “This research developed models, based on machine learning (MA), for forecasting 16 h and 4 h of occurrence of a convective meteorological event (CME), 4 h for forecasting severity and evaluating the applicability of the optimal models of 12 UTC using thermodynamic instability indices (TII) data extracted from the WRF model with two different types of parameterization configuration in an attempt to develop a 30 h CME forecast model. In the training and testing of the MA algorithms, the classic TIIs (input) were used, obtained from the atmospheric profiles of the Brasilia upper air sounding and atmospheric discharges (output) detected in the study area for the characterization of CME, considering the period from 2012 to 2017.” Financial support for this research came from Department of Airspace Control (DECEA), through the Brazilian Organization for the Scientific and Technological Development of Airspace Control (CTCEA). The news correspondents obtained a quote from the research from Federal University Rio de Janeiro, “The optimal models applied to the modeled TIIs were evaluated through statistical metrics with configuration Ⅱ obtaining significant results. For CME detection, the results showed that the best models obtained POD, 1-FAR, F-and KAPPA with values respectively greater than 0.90, 0.80, 0.90, 0.80 and BIAS ranging from 0 .89 and 1.12. For the detection of event severity, the model presented the following statistical values (in parentheses): POD (0.82), 1-FAR (0.78), F-(0.82), KAPPA (0.59) and BIAS (0.97).” According to the news reporters, the research concluded: “The results of 16 h and 4 h CME prediction hindcasts (30 days) with developed models demonstrated acceptable performance in identifying the occurrence or non-occurrence of CME and its severity for the study area.”
Rio de JaneiroBrazilSouth AmericaCyborgsEmerging TechnologiesMachine LearningFederal University Rio de Janeiro
NETL
NSTL
Springer Nature
