Using Xgboost models for daily rainfall prediction
- Rafael Grecco Sanches Universidade de São Paulo https://orcid.org/0000-0003-2471-5070
- Rodrigo Sanches Miani School of Computer Science, Federal University of Uberlandia (UFU), Minas Gerais, Brazil https://orcid.org/0000-0002-8176-8040
- Bruno César dos Santos Department of Environmental Sciences (DCAM), Federal University of São Carlos (UFSCar), São Carlos, Brazil https://orcid.org/0000-0001-8218-6803
- Rodrigo Martins Moreira Environmental Engineering Department, Federal University of Rondônia (UNIR), Rondônia, Brazil https://orcid.org/0000-0001-6794-6026
- Gustavo Zen de Figueiredo Neves aSão Carlos School of Engineering, University of São Paulo (USP), São Paulo, Brazil https://orcid.org/0000-0002-3952-8238
- Vandoir Bourscheidt Department of Environmental Sciences (DCAM), Federal University of São Carlos (UFSCar), São Carlos, Brazil https://orcid.org/0000-0001-5419-323X
- Pedro Augusto Toledo Rios Faculty of Civil Engineering, Federal University of Uberlandia (UFU), Minas Gerais, Brazil
Resumen
Los modelos de aprendizaje automático para predecir las precipitaciones diarias han ganado fuerza en los últimos años. Comprender los beneficios del uso de esta tecnología en diferentes regiones es un tema de investigación relevante. Por esta razón, este estudio tiene como objetivo evaluar los pronósticos de lluvia diaria a partir de datos climáticos entre 1983 y 2019 en Itirapina, São Paulo, Brasil. Utilizamos un novedoso algoritmo de aprendizaje automático, XGBoost (eXtreme Gradient Boosting), para crear varios modelos de predicción de lluvia diaria. Se modelaron dos tareas: la aparición de precipitación diaria (clasificación) y la cantidad de precipitación diaria (regresión). Los resultados revelaron que la aparición de precipitaciones diarias se podía predecir con una precisión de alrededor del 90%. Además, se desarrollaron modelos para predecir la cantidad de lluvia diaria con tasas de error de alrededor de 3 mm. Observamos que la precipitación en el área de estudio está directamente asociada con la radiación solar, y los pronósticos de precipitaciones y los meses correspondientes son característicos del clima tropical.
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Biografía del autor/a
Possui graduação em Matemática pela Universidade Federal de São Carlos (2005), mestrado em Engenharia Elétrica pela Universidade Estadual de Campinas (2009) e doutorado em Engenharia Elétrica pela Universidade Estadual de Campinas (2013). Durante seu doutorado, passou seis meses como pesquisador visitante no CyQL (Laboratório de Quantificação de Segurança Cibernética) da Universidade de Maryland, EUA.
Atualmente é professor adjunto da Faculdade de Computação (FACOM) da Universidade Federal de Uberlândia (UFU).
Bachelor's degree in Geography from the Federal University of Alfenas-MG (UNIFAL-MG). Master's and PhD in Environmental Engineering Sciences from the University of São Paulo (USP). Post-doctorate in progress at the Department of Environmental Sciences (DCAm) of the Federal University of São Carlos (UFSCar). I conduct research in the areas of Climatology and Geotechnologies applied to the environment. I have experience as a teacher in the area of Geography education at elementary and high school levels.
Adjunct Professor in the Department of Environmental Engineering at the Federal University of Rondônia - Ji-Paraná Campus, promoting quality public education and popularization of science. Coordinator of the Laboratory of Geomatics and Statistics (LABGET - UNIR) and leader of the Environmental Engineering Research Group (GPEA-UNIR). I am a data scientist who transforms multi-sensor remote sensing products (optical and radar) and census data into spatial information to respond to complex environmental problems related to climate science, forest fragmentation, water resources, and public health. Post-Doctorate at San Diego State University - USA (2023). Post-Doctorate in Natural Resources - UFMS (2020). PhD in Environmental Engineering Sciences - EESC/USP (2017) with a sandwich period at the University of Michigan - USA. Master in Agricultural Sciences (2014), Environmental Sanitarian (2011) and Environmental Manager (2013) from IFGoiano - Campus Rio Verde. Mastery of Google Earth Engine, R and Python for spatial data analysis.
He holds a bachelor's and a degree in Geography from UTPR. He holds a master's degree in Science from USP (2014), with a study on Urban Climate in the city of São Carlos-SP. He holds a PhD in Science from USP (2018), whose research focused on the performance of atmospheric systems and the rainfall distribution of the state of Goiás and the Federal District. He was a collaborating professor in the Postgraduate Program in Environmental Engineering Sciences at the University of São Paulo, offering two courses: [SEA5916] Introduction to the Dynamic Study of Climate Generalities and Specificities and [SEA5862] Climatology Applied to the Environment; he is an advisor for academic master's degrees in the research line Climatology Applied to the Environment. He taught courses related to geosciences at the State University of Goiás and at the Barretos Educational Foundation, in undergraduate and graduate courses. Scientific advisor and visiting researcher at the National Laboratory for Sustainable Living and Communities of the Faculty of Architecture of the Autonomous University of Chiapas (UNACH - Mexico). He is a member of the Brazilian Association of Climatology (ABClima), serving as Director-Secretary (2018-2021) and of the Deliberative Council (2023-2025; 2021-2023; 2016-2018 and 2014-2016). Member of the research group Readings and Analysis in Hydrography, Climatology and Cartography (UFRN/CNPq) and of the Locality Study Group (USP/CNPq). He has teaching and professional experience in the area of Geosciences, with interests in the themes of Geography, Climatology, Atmospheric circulation of South America, Comfort and Technology of the built environment, Engineering Geology and Environmental Geotechnics. Reviewer for national and international scientific journals.
He is an associate professor at the Department of Environmental Sciences (DCAm) and a permanent professor at the Postgraduate Program in Environmental Sciences at the Federal University of São Carlos (UFSCar), where he has worked since 2013. He has a degree in Geography from the Federal University of Santa Maria (2006), a master's degree (2008) and a doctorate (2012) in Space Geophysics (concentration in Atmospheric Sciences) from the National Institute for Space Research (INPE). He was a postdoctoral fellow at the atmospheric electricity group (ELAT) at INPE until early 2013. He was a visiting researcher at INRAE (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement), Antony, France, between 2019 and 2020. He works mainly in the area of atmospheric sciences and GIS, with an emphasis on topics such as: climatology and hydrology, storm formation, hydrological modeling, remote sensing and geoprocessing.
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