Neural Network-Based Modeling for Risk Evaluation and Early Warning for Large-Scale Sports Events
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".