| Problem Definition | Predict pedestrian wind comfort levels based on CFD simulation data. <br> - Key Parameters: wind speed, direction, building geometry, comfort level. | Cluster pedestrian zones based on wind comfort characteristics without predefined labels. <br> - Key Parameters: wind speed, direction, building geometry. |
| Data Collection | Collect CFD simulation data and corresponding comfort levels through measurements or predefined scales. | Gather CFD simulation data for pedestrian zones, including wind speed, direction, and building geometry. |
| Data Preprocessing | - Clean data: Remove outliers/errors. <br> - Feature Engineering: Create/modify features. <br> - Split data into training and testing sets. | - Clean data: Remove outliers/errors. <br> - Standardize/Normalize data to ensure uniform scale and consistency. |
| Model Selection and Training | Choose and train a Random Forest Regression model on the training dataset to predict comfort levels. | Apply K-Means Clustering to group pedestrian zones based on wind comfort characteristics. <br> Determine optimal number of clusters using the elbow method. |
| Model Evaluation and Optimization | - Evaluate using metrics like MSE/MAE. <br> - Optimize by tuning hyperparameters and validating with cross-validation. | - Analyze clusters to understand distinct comfort regimes. <br> - Label clusters based on characteristics (e.g., high, moderate, low comfort). |
| Prediction and Deployment | Use the trained model to predict pedestrian comfort levels in new CFD simulations. <br> Deploy for real-time predictions in urban planning. | - Visualize clusters on a map/chart to identify comfort zones. <br> - Use color coding to represent different clusters. |
| Results Reporting | Visualize predicted vs. actual comfort levels to assess accuracy. <br> Generate reports to communicate findings to stakeholders. | Provide recommendations for urban design improvements based on comfort zones. <br> Suggest changes to building geometries or pathways to enhance comfort. |
