@AudiovisualMaterial{CamposVelho:2023:InPaDi,
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
               author = "Campos Velho, Haroldo Fraga de",
                 city = "Ņaņa, Peru",
       conferencename = "Congreso Internacional de Matem{\'a}tica Aplicada y Computacional 
                         (CIMAC), 11",
                 date = "01-04 Aug.",
             keywords = "Weather and climate prediction is a permanent challenge. One 
                         remarkable scientific conquer was, the numerical weather 
                         prediction (NWP), where the applied mathematics and scientific 
                         computing gave, an important contribution. Nowadays, machine 
                         learning algorithms have present a very good results on, many 
                         applications. The focus of our talk is to combine the forecasting 
                         from a partial differential equation, atmospheric model with a 
                         machine learning algorithm to predict precipitation for severe 
                         episodes. The, attributes from differential equation model are 
                         selected by employing the p-value statistical hypothesis, test. 
                         The forecasting using combined approaches produces a better 
                         precipitation prediction, even for, severe Weather.",
             language = "en",
           targetfile = "CIMAC_2023-Haroldo.pdf",
                title = "Severe Weather Prediction: Integrating Partial Differential and 
                         Machine Learning Models",
                 year = "2023",
        urlaccessdate = "13 maio 2024"
}