A study on the impact of data balance on rainfall prediction through artificial neural networks using surface microwave radiometers

Keywords: Rainfall prediction, Data balancing, Machine learning, Amazon, ATTO Campina


The National Institute for Space Research (INPE) has been a partner in significant projects that conduct atmospheric investigations impacting various sectors, such as the Amazon Tall Tower Observatory (ATTO) project. Since 2009, the project has conducted studies on the interactions between climate and the Amazon forest. ATTO has played an essential role in providing large volumes of data obtained by meteorological sensors, contributing to a deeper understanding of the atmospheric dynamics of the region. In a landscape where Artificial Intelligence-based rainfall forecast models gain prominence, this study explores the imbalance of data from the ATTO Campina field experiment and its influence on short-term rainfall forecasts using Artificial Neural Networks (ANNs). Metrics such as MAE, RMSE, and POD, as well as FAR indices, were applied in the assessment and revealed the connection between data balance and forecast results. More balanced data or data with greater weights for different rainfall ranges yield better results. The study emphasizes the importance of reliable data for training rain forecast models, aiming to improve the dexterity of these models. This approach is fundamental to increase the reliability of these models in real environments.


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Author Biographies

Lourenço José Cavalcante Neto, National Institute for Space Research


Master's student in Applied Computing at the National Institute for Space Research, INPE. Holds a specialization in Informatics in Education from the Cultural Union College of the State of São Paulo (UCESP), completed in 2016, with a degree in Computing from the Federal Institute of Tocantins (IFTO), Araguatins campus, obtained in 2015. Also has technical training in Internet Informatics from IFTO, Palmas campus, obtained in 2014. Between March 2016 and May 2019, was part of the faculty at the Federal Institute of Mato Grosso (IFMT), Guarantã do Norte Advanced Campus, and is currently a permanent member of the faculty at the Federal Institute of Tocantins (IFTO), Araguatins campus. The main areas of expertise are primarily focused on Artificial Intelligence, Database, and System Development in PHP and Python languages.

Alan James Peixoto Calheiros, National Institute for Space Research (INPE)


Possui graduação em Meteorologia pela Universidade Federal de Alagoas (2006), Mestrado (2008) e Doutorado (2013) em Meteorologia pelo Instituto Nacional de Pesquisas Espaciais (INPE). Desde 2015 é Tecnologista do INPE. Entre 2015 e 2017 desenvolveu atividades na Divisão de Operações do Centro de Previsão de Tempo e Estudos Climáticos (CPTEC/INPE). Atualmente faz parte do grupo do Laboratório Associado de Computação e Matemática Aplicada (LabAC/CoCTE/INPE) e membro permanente do programa de pós-graduação do INPE em Computação Aplicada (CAP). Tem experiência na área de Meteorologia, com ênfase em Sensoriamento Remoto da Atmosfera, atuando principalmente nos seguintes temas: Sistemas Automáticos de Previsão a Curto Prazo de Tempestades, Estimativa de Precipitação por Satélites e Radares Meteorológicos, Microfísica de Nuvens e Precipitação e Modelagem dos Processos Radiativos para Sensoriamento Remoto da Atmosfera


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How to Cite
L. Cavalcante Neto and A. Calheiros, “A study on the impact of data balance on rainfall prediction through artificial neural networks using surface microwave radiometers”, LAJC, vol. 11, no. 2, pp. 51-59, Jul. 2024.
Research Articles for the Regular Issue