Accident prediction in construction using hybrid wavelet-machine learning

Kerim Koc*, Ömer Ekmekcioğlu, Asli Pelin Gurgun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


Occupational accident rates in construction projects are usually higher than other industries in most countries, even though safety management systems are continuously improving. This study aims to contribute to the body of construction safety management by coupling discrete wavelet transform (DWT) and different machine learning (ML) methods to predict the number of occupational accidents using time series data. A dataset that consists of 393,160 occupational accidents recorded in Turkey between 2012 and 2020 was analyzed to predict the number of accidents for short-term, mid-term and long-term time periods, 1-day, 7-day and 30-day ahead, respectively. Model performances of stand-alone ML algorithms are improved with DWT, and hybrid wavelet-ANN showed the best performance. A dynamic utilization plan was proposed to the field of safety management by introducing a new theoretical and practical framework. This study also aims to fill the gap in the literature related to time series prediction models in construction safety management.

Original languageEnglish
Article number103987
JournalAutomation in Construction
Publication statusPublished - Jan 2022

Bibliographical note

Publisher Copyright:
© 2021


  • Artificial intelligence
  • Artificial neural network
  • Machine learning
  • Multi-variate adaptive regression splines
  • Occupational health and safety
  • Safety management
  • Support vector regression
  • Time series
  • Wavelet decomposition


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