External Climate Data Extraction Using the Forward Feature Selection Method in the Context of Occupational Safety Chapter Conference Paper uri icon


  • 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.
  • This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the Project Scope UIDB/05757/2020 and NORTE-01-0247-FEDER 072598 iSafety: Intelligent system for occupational safety and well-being in the retail sector.

publication date

  • 2022