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External climate data extraction using the forward feature selection method in the context of occupational safety
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Orientador(es)
Resumo(s)
Global climate changes and the increase in average temperatures are some of the major contemporary problems that have not been considered in the context of external factors to increase accident risk.
Studies that include climate information as a safety parameter in machine learning models designed to predict the occurrence of accidents are
not usual. This study aims to create a dataset with the most relevant climatic elements, to get better predictions. The results will be applied
in future studies to correlate with the accident history in a retail sector company to understand its impact on accident risk. The information
was collected from the National Oceanic and Atmospheric Administration (NOAA) climate database and computed by a wrapper method to
ensure the selection of the most features. The main goal is to retain all
the features in the dataset without causing significant negative impacts
on the prediction score.
Descrição
Palavras-chave
Weather conditions Data extraction Occupational hazard prediction Machine learning Data mining
Contexto Educativo
Citação
Silva, Felipe G.; Sena, Inês; Lima, Laires A.; Fernandes, Florbela P.; Pacheco, Maria F.; Vaz, Clara B.; Lima, José; Pereira, Ana I. (2022). External climate data extraction using the forward feature selection method in the context of occupational safety. In 22nd International Conference on Computational Science and Its Applications , ICCSA 2022. Malaga. 13378, p. 3-14