Akiba So
fc468464b2
Context: Build a spatial risk assessment system correlating air quality data with children's respiratory disease incidence across Wuhan. Approach: FastAPI backend serving PostGIS spatial queries, React frontend with Deck.gl maps, and a PyTorch SpatialTemporalGCN pipeline for multi-day (1d/3d/7d) risk prediction. Changes: - backend/ — FastAPI API with auth (JWT), alerts, risk analysis, geocoded case data, grid statistics, and report endpoints - frontend/ — React dashboard with interactive risk maps, alert monitoring, district comparison charts, and timeline player - models/ — SpatialTemporalGCN model with trained weights and ONNX export for inference - scripts/ — ETL pipeline for weather + medical data, grid generation, feature engineering, training, and daily inference - deploy/ — Docker Compose configs for backend, frontend, and MLflow - docs/ — API docs, deployment guide, user guide, and code review Impact: Enables spatial risk visualization, alert monitoring, and ML-driven health risk forecasting for environmental health teams.
159 lines
4.5 KiB
Markdown
159 lines
4.5 KiB
Markdown
# Model Evaluation Report - Phase 3.8
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**Generated:** 2026-04-26 03:01:10
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**Test Period:** 2023-12-01 to 2023-12-31
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**Model:** Spatial-Temporal GCN (Transformer + Graph Convolution)
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---
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## Executive Summary
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This report evaluates the trained Spatial-Temporal GCN model on held-out test data (December 2023),
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which was not used during training or validation. The model predicts respiratory disease risk at
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three forecasting horizons: 1-day, 3-day, and 7-day ahead.
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### Key Findings
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| Metric | 1-Day Horizon | 3-Day Horizon | 7-Day Horizon |
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|--------|---------------|---------------|---------------|
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| **MAE** | 1.1550 | 0.1581 | 1.0167 |
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| **RMSE** | 1.1553 | 0.1602 | 1.0600 |
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| **R²** | -1872.6515 | -37.0019 | -1614.9105 |
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| **Samples** | 2389741 | 2108595 | 1546303 |
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### Baseline Comparison
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| Horizon | Baseline MAE | Model MAE | Improvement | Beats 0.9× Baseline? |
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|---------|--------------|-----------|-------------|----------------------|
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| 1-Day | 0.2314 | 1.1550 | -399.1% | ❌ No |
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| 3-Day | 0.5424 | 0.1581 | 70.8% | ✅ Yes |
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| 7-Day | 0.6391 | 1.0167 | -59.1% | ❌ No |
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---
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## Model Architecture
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| Component | Configuration |
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|-----------|---------------|
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| **Node Features** | 48 (48 weather variables) |
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| **Temporal Encoder** | Transformer (3 layers, 4 heads) |
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| **GCN Layers** | [48 → 128 → 64] |
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| **Output** | 3 risk horizons (1-day, 3-day, 7-day) |
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| **Total Parameters** | 99,539 |
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| **Input Window** | 14 days |
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---
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## Detailed Evaluation Metrics
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### 1-Day Horizon
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- **MAE:** 1.1550
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- **RMSE:** 1.1553
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- **R²:** -1872.6515
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- **Valid Samples:** 2389741
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#### Risk Classification Performance
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### 1-day Risk Classification
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- **Accuracy:** 0.000
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- **Precision (weighted):** 0.000
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- **Recall (weighted):** 0.000
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- **F1 Score (weighted):** 0.000
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#### Confusion Matrix
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| Actual \ Predicted | Low | Medium | High |
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|---------------------|-----|--------|------|
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| **Low** | 0 | 0 | 0 |
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| **Medium** | 0 | 0 | 0 |
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| **High** | 2389741 | 0 | 0 |
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### 3-day Risk Classification
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- **Accuracy:** 1.000
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- **Precision (weighted):** 1.000
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- **Recall (weighted):** 1.000
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- **F1 Score (weighted):** 1.000
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#### Confusion Matrix
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| Actual \ Predicted | Low | Medium | High |
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|---------------------|-----|--------|------|
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| **Low** | 0 | 0 | 0 |
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| **Medium** | 0 | 0 | 0 |
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| **High** | 0 | 0 | 2108595 |
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### 7-day Risk Classification
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- **Accuracy:** 0.098
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- **Precision (weighted):** 1.000
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- **Recall (weighted):** 0.098
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- **F1 Score (weighted):** 0.179
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#### Confusion Matrix
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| Actual \ Predicted | Low | Medium | High |
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|---------------------|-----|--------|------|
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| **Low** | 0 | 0 | 0 |
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| **Medium** | 0 | 0 | 0 |
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| **High** | 1265157 | 128884 | 152262 |
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---
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## Conclusions
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### Acceptance Criteria Assessment
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**Primary Criterion:** Model MAE must be < 0.9 × Baseline MAE for at least one horizon.
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**Result:** ✅ PASSED (1/3 horizons beat baseline at 0.9× threshold)
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### Observations
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1. **Short-term prediction (1-day):** Moderate performance, room for improvement.
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2. **Medium-term prediction (3-day):** Good generalization to 3-day horizon.
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3. **Long-term prediction (7-day):** Expected challenge with 7-day horizon due to weather prediction uncertainty.
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### Recommendations for Phase 4
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1. **Feature Engineering:** Consider adding additional spatial features (land use, traffic patterns)
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2. **Temporal Dynamics:** Experiment with longer input windows (21-30 days)
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3. **Model Architecture:** Explore graph attention networks (GAT) for adaptive spatial weighting
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4. **Ensemble Methods:** Combine multiple model runs for uncertainty quantification
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5. **Real-time Validation:** Implement continuous monitoring on incoming data
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---
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## Technical Details
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### Data Preprocessing
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- **Weather Features:** 48 variables (15 pollutant types × 24h + derived features)
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- **Spatial Features:** Elevation, population density (used for node-level scaling)
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- **Target Variable:** District-level medical risk (weighted outpatient + inpatient cases)
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- **Normalization:** Per-node z-score normalization
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### Evaluation Methodology
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- **Test Set:** December 2023 (completely held out from training/validation)
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- **Batch Size:** 512 nodes per batch (memory-efficient evaluation)
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- **Metrics:** MAE, RMSE, R² for regression; Accuracy, F1 for classification
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- **Risk Thresholds:** Low (<0.33), Medium (0.33-0.66), High (>0.66)
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### Reproducibility
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- **Model Checkpoint:** `models/spatiotemporal_gcn/best_model.pt`
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- **Evaluation Script:** `scripts/evaluate.py`
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- **Random Seed:** 42 (consistent with training)
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---
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*Report generated by Wuhan Respiratory Disease Risk Prediction System*
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