@AudiovisualMaterial{CuadrosAlCoCaGuDaVi:2025:EsAtTu,
             abstract = "Accurate estimation of the refractive index structure parameter 
                         (C\ₙ˛) is vital for optimizing astronomical observations 
                         and adaptive optics. This study explores machine learning models 
                         to predict C\ₙ˛ from Differential Image Motion Monitor 
                         (DIMM) data using ERA5 reanalysis from ECMWF. Three models were 
                         tested: Multilayer Perceptron (MLP), Long Short-Term Memory 
                         (LSTM), and Extreme Gradient Boosting (XGBoost). Data 
                         preprocessing aligned time series and addressed missing values. 
                         Training and validation were performed with data from Paranal 
                         Observatory, Chile. The models will later be applied at the 
                         Laborat{\'o}rio Nacional de Astrof{\'{\i}}sica (LNA), Brazil. 
                         ERA5-derived features included temperature, pressure, humidity, 
                         and wind profiles; the target was DIMM-measured C\ₙ˛. 
                         Results demonstrate the potential of combining reanalysis data and 
                         machine learning for atmospheric turbulence estimation, offering a 
                         cost-effective alternative to continuous in-situ monitoring.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and {} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
               author = "Cuadros, Edith Tueros and Almeida, A. and Cornejo, D. and 
                         Carlesso, Franciele and Guarnieri, Fernando Luis and Dal Lago, 
                         Alisson and Vieira, Luis Eduardo Antunes",
                 city = "S{\~a}o Jos{\'e} dos Campos",
       conferencename = "Semana Acad{\^e}mica da Geof{\'{\i}}sica Espacial (SAGE)",
                 date = "22 a 26 set. 2025",
             language = "en",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
     publisheraddress = "S{\~a}o Jos{\'e} dos Campos",
                  ibi = "8JMKD2USPTW34P/4EDB9UH",
                  url = "http://urlib.net/ibi/8JMKD2USPTW34P/4EDB9UH",
           targetfile = "SAGE-Poster2025.pdf",
                title = "Estimating Atmospheric Turbulence (Cn˛) at Astronomical Sites 
                         Using Meteorological Data and Machine Learning",
                 type = "Poster",
                 year = "2025",
        urlaccessdate = "2025, Nov. 15"
}