Neural Network-Based Modeling for Risk Evaluation and Early Warning for Large-Scale Sports Events
Abstract
:1. Introduction and Literature Review
2. The Establishment of an Index System for Risk Evaluation of Large-Scale Sports Events
3. The Basic Principles and Steps of Neural Network Modeling
4. The Establishment of the BPNN Model for Risk Evaluation and Early Warning of Large-Scale Sports Events
4.1. The Generation of New Samples
4.2. The Establishment of a BPNN Model with Strong Generalization Ability
4.2.1. Determining a Reasonable Network Structure
4.2.2. Establishing a BPNN Model with Good Generalization
4.2.3. Analysis of the Effects of Each Evaluation Indicator on the Risks of Large-Scale Sports Events
4.3. Interval Distribution of the Output Value of the Established BPNN Model for Different Risk Levels
5. Empirical Results
5.1. Risk Evaluation for the 28 Actual Large-Scale Sports Events
5.2. Results and Risk Level Assessment
6. Discussions
6.1. How to Establish a Robust BPNN Model
6.2. How to Reduce the Risk of Hosting Large-Scale Sports Events
6.3. Comparison with Clustering Methods
6.4. The Limitation of the Study
7. Conclusions and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Samples | Risk Level | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
S1 | 4 | 2 | 2 | 4 | 4 | 5 | 3 | 3 | 2 | 3 | 3 | 4 | moderate |
S2 | 4 | 1 | 4 | 2 | 2 | 3 | 4 | 1 | 4 | 5 | 2 | 2 | severe |
S3 | 1 | 4 | 2 | 3 | 2 | 1 | 1 | 4 | 2 | 1 | 5 | 4 | mild |
S4 | 1 | 4 | 1 | 1 | 1 | 3 | 1 | 5 | 2 | 2 | 5 | 2 | mild |
S5 | 3 | 2 | 5 | 1 | 2 | 3 | 4 | 1 | 5 | 5 | 1 | 4 | severe |
S6 | 4 | 2 | 5 | 2 | 1 | 3 | 4 | 2 | 2 | 4 | 2 | 4 | mild |
S7 | 3 | 3 | 2 | 2 | 4 | 4 | 2 | 4 | 3 | 3 | 3 | 5 | moderate |
S8 | 4 | 2 | 5 | 1 | 4 | 4 | 5 | 2 | 5 | 5 | 2 | 3 | severe |
S9 | 5 | 3 | 1 | 2 | 4 | 4 | 3 | 2 | 4 | 4 | 2 | 5 | moderate |
S10 | 4 | 2 | 5 | 2 | 2 | 2 | 5 | 2 | 2 | 5 | 2 | 4 | mild |
S11 | 5 | 1 | 4 | 1 | 2 | 2 | 5 | 2 | 1 | 4 | 3 | 5 | mild |
S12 | 4 | 3 | 4 | 4 | 3 | 4 | 3 | 3 | 2 | 2 | 5 | 4 | moderate |
S13 | 2 | 4 | 2 | 5 | 2 | 2 | 3 | 4 | 1 | 1 | 4 | 1 | mild |
S14 | 2 | 1 | 5 | 1 | 3 | 2 | 5 | 1 | 5 | 4 | 1 | 3 | severe |
S15 | 1 | 5 | 1 | 4 | 1 | 2 | 1 | 5 | 1 | 1 | 4 | 2 | mild |
S16 | 1 | 4 | 2 | 5 | 2 | 3 | 3 | 5 | 1 | 2 | 5 | 1 | mild |
S17 | 2 | 5 | 1 | 5 | 4 | 1 | 2 | 5 | 1 | 2 | 5 | 2 | mild |
S18 | 5 | 4 | 3 | 5 | 5 | 3 | 2 | 3 | 3 | 5 | 3 | 5 | moderate |
S19 | 2 | 5 | 1 | 4 | 3 | 1 | 2 | 5 | 1 | 2 | 5 | 2 | mild |
S20 | 2 | 5 | 1 | 4 | 2 | 2 | 3 | 5 | 2 | 1 | 4 | 2 | mild |
S21 | 3 | 4 | 3 | 2 | 5 | 4 | 4 | 1 | 5 | 4 | 1 | 4 | moderate |
S22 | 4 | 3 | 2 | 4 | 5 | 4 | 2 | 2 | 3 | 2 | 4 | 3 | moderate |
S23 | 5 | 4 | 3 | 3 | 4 | 5 | 3 | 4 | 2 | 2 | 2 | 3 | severe |
S24 | 1 | 5 | 2 | 5 | 3 | 1 | 1 | 5 | 1 | 5 | 5 | 3 | mild |
S25 | 3 | 5 | 1 | 5 | 3 | 4 | 3 | 5 | 1 | 2 | 4 | 1 | mild |
S26 | 1 | 4 | 1 | 5 | 2 | 3 | 1 | 5 | 2 | 2 | 5 | 1 | mild |
S27 | 3 | 2 | 3 | 2 | 3 | 3 | 5 | 2 | 3 | 4 | 2 | 3 | severe |
S28 | 1 | 5 | 1 | 5 | 3 | 1 | 2 | 5 | 1 | 2 | 5 | 2 | mild |
Risk Level * | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
The maximum value | 1 | 5 | 5 | 5 | 5 | 4 | 4 | 5 | 2 | 5 | 5 | 5 |
2 | 5 | 4 | 4 | 5 | 5 | 5 | 4 | 5 | 5 | 5 | 5 | |
3 | 5 | 4 | 5 | 3 | 4 | 5 | 5 | 5 | 5 | 2 | 4 | |
The minimum value | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 |
2 | 3 | 2 | 1 | 2 | 3 | 3 | 2 | 2 | 2 | 1 | 3 | |
3 | 2 | 1 | 3 | 1 | 2 | 2 | 3 | 2 | 2 | 1 | 2 | |
The mean value | 1 | 2.07 | 4 | 2 | 3.73 | 2.2 | 2.07 | 2.47 | 1.4 | 2.4 | 4.2 | 2.4 |
2 | 4 | 3.14 | 2.43 | 3.29 | 4.29 | 4 | 2.71 | 3.14 | 3.29 | 3 | 4.29 | |
3 | 3.5 | 2 | 4.17 | 1.67 | 3 | 3.33 | 4.33 | 4 | 4.17 | 1.67 | 3 | |
Standard deviation | 1 | 1.33 | 1.31 | 1.46 | 1.53 | 0.86 | 0.96 | 1.41 | 0.51 | 1.40 | 1.08 | 1.30 |
2 | 0.82 | 0.69 | 0.98 | 1.25 | 0.76 | 0.58 | 0.76 | 1.07 | 1.11 | 1.29 | 0.76 | |
3 | 1.05 | 1.10 | 0.98 | 0.82 | 0.89 | 1.03 | 0.82 | 1.26 | 1.17 | 0.52 | 0.63 |
Type of Samples | Mean Value | Standard Deviation |
---|---|---|
Training dataset (Tr) | 1.992 | 0.8275 |
Validation dataset (Va) | 1.990 | 0.7984 |
Test dataset (Te) | 2.029 | 0.8162 |
Ranking | 12 | 11 | 9 | 8 | 2 | 4 | 5 | 3 | 1 | 7 | 10 | 6 |
Error * | 0.085 | 0.099 | 0.105 | 0.125 | 0.282 | 0.173 | 0.150 | 0.278 | 0.444 | 0.130 | 0.104 | 0.146 |
Ratio ** | 1.069 | 1.246 | 1.328 | 1.576 | 3.554 | 2.185 | 1.888 | 3.510 | 5.597 | 1.640 | 1.307 | 1.846 |
Simulation Samples | Output Value | Risk Level | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SS1 | 2 | 4 | 2 | 4 | 2 | 2 | 2 | 4 | 1 | 2 | 4 | 2 | 0.9116 | mild |
SS2 | 4 | 3 | 2 | 3 | 4 | 4 | 3 | 3 | 3 | 3 | 3 | 4 | 2.0538 | moderate |
SS3 | 4 | 2 | 4 | 2 | 3 | 3 | 4 | 2 | 4 | 4 | 2 | 3 | 3.0219 | severe |
SS4 | 2 | 5 | 3 | 4 | 3 | 2 | 2 | 5 | 3 | 2 | 5 | 5 | 1.4860 | mild |
SS5 | 2 | 5 | 2 | 2 | 2 | 4 | 2 | 5 | 3 | 3 | 5 | 3 | 1.7443 | moderate |
SS6 | 3 | 5 | 3 | 5 | 3 | 3 | 3 | 5 | 2 | 3 | 5 | 3 | 0.9188 | mild |
SS7 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 3 | 1 | 1 | 3 | 1 | 0.9202 | mild |
SS8 | 5 | 3 | 3 | 5 | 5 | 5 | 4 | 4 | 3 | 4 | 4 | 5 | 1.9826 | moderate |
SS9 | 4 | 4 | 3 | 3 | 5 | 5 | 3 | 5 | 4 | 4 | 4 | 5 | 2.0751 | moderate |
SS10 | 5 | 4 | 3 | 4 | 5 | 5 | 4 | 4 | 4 | 4 | 4 | 5 | 2.0196 | moderate |
SS11 | 3 | 2 | 1 | 2 | 3 | 3 | 2 | 2 | 2 | 2 | 2 | 3 | 2.0831 | moderate |
SS12 | 5 | 2 | 5 | 3 | 3 | 4 | 5 | 2 | 5 | 5 | 3 | 3 | 3.0539 | severe |
SS13 | 4 | 3 | 5 | 2 | 3 | 4 | 5 | 2 | 5 | 5 | 2 | 5 | 2.8962 | severe |
SS14 | 5 | 3 | 5 | 3 | 4 | 4 | 5 | 3 | 5 | 5 | 3 | 4 | 2.9912 | severe |
SS15 | 3 | 1 | 3 | 1 | 2 | 2 | 3 | 1 | 3 | 3 | 1 | 2 | 2.9887 | severe |
Samples | O1 + | O2 | O3 | Risk Level | |
---|---|---|---|---|---|
Case 1 * | A ** | 0.0023 | 0.5116 | 0.5917 ++ | Severe |
B | 0.8406 | 0.0025 | 0.011 | Mild | |
C | 0.035 | 0.6528 | 0.0176 | Moderate | |
D | 0.0054 | 0.9767 | 0.0122 | Moderate | |
Case 2 | A | 0.0035 | 0.0858 | 0.9219 | Severe |
B | 0.5944 | 0.0005 | 0.0456 | Mild | |
C | 0.0111 | 0.2583 | 0.2417 | Moderate | |
D | 0.0033 | 0.8949 | 0.0799 | Moderate | |
Case 3 | A | 0.0019 | 0.062 | 0.9395 | Severe |
B | 0.3796 | 0.0027 | 0.5483 | Severe | |
C | 0.0202 | 0.0085 | 0.9306 | Severe | |
D | 0.0031 | 0.0655 | 0.8941 | Severe | |
Case 4 | A | 0.0055 | 0.6471 | 0.1056 | Moderate |
B | 0.7550 | 0.0042 | 0.1059 | Mild | |
C | 0.6956 | 0.0707 | 0.0056 | Mild | |
D | 0.2617 | 0.6287 | 0.0012 | Moderate |
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Zhong, C.; Lou, W.; Wang, C. Neural Network-Based Modeling for Risk Evaluation and Early Warning for Large-Scale Sports Events. Mathematics 2022, 10, 3228. https://doi.org/10.3390/math10183228
Zhong C, Lou W, Wang C. Neural Network-Based Modeling for Risk Evaluation and Early Warning for Large-Scale Sports Events. Mathematics. 2022; 10(18):3228. https://doi.org/10.3390/math10183228
Chicago/Turabian StyleZhong, Chenghao, Wengao Lou, and Chuting Wang. 2022. "Neural Network-Based Modeling for Risk Evaluation and Early Warning for Large-Scale Sports Events" Mathematics 10, no. 18: 3228. https://doi.org/10.3390/math10183228
APA StyleZhong, C., Lou, W., & Wang, C. (2022). Neural Network-Based Modeling for Risk Evaluation and Early Warning for Large-Scale Sports Events. Mathematics, 10(18), 3228. https://doi.org/10.3390/math10183228