Neural Network-Based Modeling for Risk Evaluation and Early Warning for Large-Scale Sports Events

Abstract

[Problem] The risks of hosting large-scale sports events are very difficult to evaluate and often directly affected by natural environment risks, events management risks, and social environment risks. Before hosting the events, accurately assessing these risks can effectively minimize the occurrence of risks and reduce the subsequent losses. [Aim] In this article, we advocate the use of a back propagation neural network (BPNN) model for risk evaluation and early warning of large-scale sports events. [Methods] We first use expert surveys to assess the risks of 28 large-scale sports events using 12 indicators associated with climate conditions, events management, and natural disasters. We then apply the BPNN model to evaluate the risks of 28 large-scale sports events with sufficient samples by adding white noise with mean zero and small variance to the small actual samples. We provide a general rule to establish a BPNN model with insufficient and small samples. [Results] Our research results show that the recognition accuracy of the established BPNN model is 86.7% for the 15 simulation samples and 100% for the 28 actual samples. Based on this BPNN model, we determined and ranked the risk level of the events and the importance of each indicator. Thus, sample S8 had the highest risk and the second highest was sample S14, and indicator nine was the most important and indicator one the least important. [Conclusions] We can apply the established BPNN model to conveniently evaluate the risk of hosting a large-scale sports event. By analyzing the nonlinear relationship between each indicator and the risk of the sports event, and applying the established BPNN model, we can propose more targeted and effective measures and suggestions for eliminating and decreasing the risks of hosting a large-scale sports event, and ensure large-scale sports events can be successfully hosted.

An Exploration of Prediction Performance Based on Projection Pursuit Regression in Conjunction with Data Envelopment Analysis: A Comparison with Artificial Neural Networks and Support Vector Regression

“…To establish MLs such as BPNN, except for [8], no other authors divided the samples into training, validation, and test datasets with similar properties. No study discusses how, according to the error changes of the validation dataset, to use the early-stopping or regularization methods to prevent overtraining, and use the trial-and-error approach to determine a reasonable number of hidden-layer neurons to ensure the generalization ability and practical value [6,[39][40][41][42][43].…”

Short Review of Intention Mining in Social Crisis Management through Automatic Technologies

“…Guo et al [32] used video reconstruction to locate security threats. Zhong et al [33] developed a security risk assessment system for sporting events using neural networks. Li et al [34] studied public risk perception and emotion expression during the Covid-19 pandemic to assist in managing public health risks.…”

A Projection Pursuit Dynamic Cluster Model for Tourism Safety Early Warning and Its Implications for Sustainable Tourism

“…(2) BPNN model. Artificial neural networks are the most popular machine learning algorithm chosen to perform a risk assessment and safety early warning[55][56][57][58][59][60], particularly the BPNN model. Establishing a BPNN model with generalization ability and practical value must follow the required principles and steps[28,51,53,54,61].First, the BPNN model is only suitable for modeling with large sample data, and it faces the defect of the "curse of dimensionality".…”