@Article{SilvaFranRuivCamp:2022:WRMaLe,
               author = "Silva, Yasmin Uch{\^o}a da and Fran{\c{c}}a, Gutemberg Borges 
                         and Ruivo, Heloisa Musetti and Campos Velho, Haroldo Fraga de",
          affiliation = "{Universidade Federal do Rio de Janeiro (UFRJ)} and {Universidade 
                         Federal do Rio de Janeiro (UFRJ)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Forecast of convective events via hybrid model: WRF and machine 
                         learning algorithms",
              journal = "Applied Computing and Geosciences",
                 year = "2022",
               volume = "16",
                pages = "e100099",
                month = "Dec.",
             keywords = "Atmospheric discharge, Convective event, Data mining, Forecast, 
                         Machine learning.",
             abstract = "This presents a novel hybrid 24-h forecasting model of convective 
                         weather events based on numerical simulation and machine learning 
                         algorithms. To characterize the convective events, 13-year from 
                         2008 up to 2020 of precipitation data from the main airport 
                         stations in Rio de Janeiro, Brazil, and atmospheric discharges 
                         from the surrounding area of around 150 km are investigated. The 
                         Weather Research and Forecasting (WRF) model was used to 
                         numerically simulate atmospheric conditions for every day in 
                         February, as it is the month with the greatest daily rate of 
                         atmospheric discharge for the data period. The p-value hypothesis 
                         test (with \α=0.05) was applied to each grid point of the 
                         numerically predicted variables (defined as an independent 
                         attribute) to find those most associated with convective events 
                         using the output of the 3-D WRF grid. This one identified 36 
                         attributes (or predictors) that were used as input in the machine 
                         learning algorithms' training-test process in this study. Several 
                         cross-validation training and testing experiments were carried out 
                         using the nine-selected categorical machine learning algorithms 
                         and the 36 defined predictors. After applying the boosting 
                         technique to the nine previously trained-tested algorithms, the 
                         results of the 24-h predictions of convective occurrences were 
                         deemed satisfactory. The RandomForest method produced the best 
                         results, with statistics values close to perfection, such as POD = 
                         1.00, FAR = 0.02, and CSI = 0.98. The 24-h hindcast utilizing the 
                         nine algorithms for the 28 days of February 2019 was very 
                         encouraging because it was able to almost recreate the maturation 
                         phase of events and their eventual failures were noted during the 
                         formation and dissipation phases. The best and worst 24-h hindcast 
                         had POD = 0.97 and 0.88, FAR = 0.02 and 0.12, and CSI = 0.94 and 
                         0.78, respectively.",
                  doi = "10.1016/j.acags.2022.100099",
                  url = "http://dx.doi.org/10.1016/j.acags.2022.100099",
                 issn = "2590-1974",
             language = "en",
           targetfile = "1-s2.0-S2590197422000210-main.pdf",
        urlaccessdate = "12 maio 2024"
}