Published Paper Details:

The purpose of this study is to explore the feasibility of applying hybrid wavelet-machine learning to make predictions about the occurrence of accidents in the construction industry. Building remains one of Canada's deadliest industry despite recent attempts to enhance safety on construction sites. Safety on construction sites may be improved by the development of an Accident Warning System that can anticipate probable incidents and inform those who are at risk [1]. Workers' safety may be improved by keeping an eye on moving items in real time and giving rapid feedback to them on the results. It is a wavelet-machine learning model that is suggested. Using this model, we can anticipate where the observed items will be in the future. An accident warning is sent when the possibility for an accident has been recognized via the analysis of certain data points. As a result, the model is able to forecast accidents and notify those affected far in advance of their occurrence, giving them plenty of time to react [1]. The decision to issue a warning is made based on the projected repercussions of a possible mishap. Construction sites may benefit from the suggested system's ability to foresee and avoid accidents. It may be used to monitor construction site activities in order to limit the number of accidents and deaths on the job site.