Comparative Analysis of Machine Learning Models for Predicting Contaminant Concentration Distributions in Hospital Wards
Abstract
1. Introduction
2. Method
2.1. Correlation Analysis
2.2. Machine Learning
2.2.1. Multiple Linear Regression
2.2.2. Support Vector Regression
2.2.3. Backpropagation Neural Network
2.2.4. Convolutional Neural Network
2.3. Predictive Evaluation Indicators
2.4. Machine Learning Model Construction Process
3. Dataset Acquisition
3.1. Indoor Environment Settings and Model Validation
3.2. Preparing the Dataset
4. Results and Discussion
4.1. Regional Contamination Correlation Analysis
4.2. Indoor Contamination Concentration Prediction
4.3. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Surface | Boundary Conditions |
---|---|
Interior walls, floors | Wall; Adiabatic |
Exterior walls | Wall; Heat flux: 35.89 W/m2 |
Exterior windows | Wall; Heat flux: 389 W/m2 |
Lamp | Wall; Heat flux: 133 W/m2 |
Patients and health care workers | Wall; Heat flux: 45 W/m2 [11] |
Armarium | Wall; Heat flux: 389.71 W/m2 |
Mouth | Velocity-inlet: 0.88 m/s [32,33]; Temperature: 34 °C [34] |
New air vent | Velocity-inlet: 1.13 m/s; Temperature: 18.64 °C |
Ceiling | Wall; Temperature: 19 °C |
Exhaust air vent | EA1, EA2, Velocity-inlet: −0.96 m/s; EA3 Velocity-inlet: −0.95 m/s [35] |
Scenario | Air Changes Under a Steady State | Air Changes After State Changes | Scenario | Air Changes Under a Steady State | Air Changes After State Changes |
---|---|---|---|---|---|
Scenario 1 | 3 | 3 | Scenario 12 | 12 | 6 |
Scenario 2 | 4 | 4 | Scenario 13 | 12 | 9 |
Scenario 3 | 5 | 5 | Scenario 14 | 9 | 3 |
Scenario 4 | 6 | 6 | Scenario 15 | 9 | 6 |
Scenario 5 | 7 | 7 | Scenario 16 | 9 | 12 |
Scenario 6 | 8 | 8 | Scenario 17 | 6 | 3 |
Scenario 7 | 9 | 9 | Scenario 18 | 6 | 9 |
Scenario 8 | 10 | 10 | Scenario 19 | 6 | 12 |
Scenario 9 | 11 | 11 | Scenario 20 | 3 | 6 |
Scenario 10 | 12 | 12 | Scenario 21 | 3 | 9 |
Scenario 11 | 12 | 3 | Scenario 22 | 3 | 12 |
Case | Sensor Arrangement Area | Sensor Number |
---|---|---|
Case 1 | E | Sensor 1 |
Case 2 | E,A-1 | Sensors 1–2 |
Case 3 | E,A-1,B-1 | Sensors 1–3 |
Case 4 | E,A-1,B-1,C-1 | Sensors 1–4 |
Case 5 | E,A-1,B-1,C-1,C-4 | Sensors 1–5 |
Case 6 | E,A-1,B-1,C-1,C-4,B-4 | Sensors 1–6 |
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Zhou, C.; Ding, Y. Comparative Analysis of Machine Learning Models for Predicting Contaminant Concentration Distributions in Hospital Wards. Buildings 2025, 15, 1828. https://doi.org/10.3390/buildings15111828
Zhou C, Ding Y. Comparative Analysis of Machine Learning Models for Predicting Contaminant Concentration Distributions in Hospital Wards. Buildings. 2025; 15(11):1828. https://doi.org/10.3390/buildings15111828
Chicago/Turabian StyleZhou, Chonggang, and Yunfei Ding. 2025. "Comparative Analysis of Machine Learning Models for Predicting Contaminant Concentration Distributions in Hospital Wards" Buildings 15, no. 11: 1828. https://doi.org/10.3390/buildings15111828
APA StyleZhou, C., & Ding, Y. (2025). Comparative Analysis of Machine Learning Models for Predicting Contaminant Concentration Distributions in Hospital Wards. Buildings, 15(11), 1828. https://doi.org/10.3390/buildings15111828