The impact of environmental factors to skiing injuries: Bayesian regularization neural network model for predicting skiing injuries

Skiing is a winter sport that is found very attractive to many people. Nevertheless, this sport is considered among high-risk sports due to the potential danger of severe injury or death. This is because of variable weather and terrain conditions, obstacles including other skiers, high speeds, trees, etc. Artificial Neural Networks have many applications in predicting the occurrence of various accident severities. In this article, we study the impact of the environmental factors to potential risk factor assessment in skiing. Hence, we apply the Bayesian Regularization Back Propagation neural network (BRBP) to predict the number of severe injuries in skiing, based on the data obtained from our prototype ski-injury registration system, the estimated bindings of environmental conditions, and the potential risk for resulting number of personal injuries. Through comparing with Levenberg Marquardt Back Propagation (LMBP), in terms of prediction accuracy, our experimental results show that BRBP has better performance by achieving higher predictive accuracy.