Overview¶
The HazEvalHub provides comprehensive frameworks and tools for evaluating hazard assessments and validating model performance. It ensures that hazard predictions are reliable, accurate, and actionable.
We are developing two avenues for evaluation of the model performance
Extreme event monitoring: classification, regression, segmentation (e.g., detection of landslides, floods, earthquakes)
Surrogate of Physical Models trained on physics-based simulations, a framework based on the AI Institute for Dynamical System Common Task Framework (collab with Nathan Kutz and Kaggle) with fair evaluation and hidden data set.
Leaderboard: a set of hazard-relevant evaluation metrics when using geospatial and terrestrial networks in collaboration with AI2.
Evaluation Framework (TBD)¶
Components¶
Performance Metrics: Standardized metrics for model evaluation
Validation Protocols: Rigorous testing procedures
Benchmarking: Comparison against established baselines
Uncertainty Quantification: Assessment of prediction confidence
Operational Testing: Real-world performance evaluation
Evaluation Metrics¶
Validation Protocols¶
Cross-Validation¶
Temporal Cross-Validation¶
For time-dependent hazard data:
Spatial Cross-Validation¶
For spatially-correlated data.
Hold-out Testing¶
Geographic hold-out: Testing on different regions
Temporal hold-out: Testing on future time periods
Event-based hold-out: Testing on specific hazard events
Benchmarking¶
Baseline Models¶
We will provide standard baselines for comparison (e.g., statistical baselines, classic ML models).
Performance Comparison¶
Uncertainty Quantification¶
Probabilistic Evaluation¶
Case Studies¶
Quality Assurance¶
Model Validation Checklist¶
Before deployment, models must pass:
Cross-validation on training data
Hold-out test performance meets thresholds
Spatial/temporal generalization verified
Uncertainty properly quantified
Edge cases and failure modes identified
Computational efficiency acceptable
Documentation complete
Operational Evaluation¶
Real-time Monitoring¶
Feedback Integration¶
Collect user feedback on predictions
Incorporate new observations for continuous evaluation
Update models based on operational experience
Evaluation Standards¶
We follow established standards:
WMO Guidelines: For meteorological hazards
USGS Standards: For seismic hazards
ISO Standards: For risk assessment
ML Best Practices: For model evaluation
Contributing¶
Help improve our evaluation framework:
Suggest new metrics for specific hazard types
Contribute validation datasets
Share evaluation protocols from your research
Report issues or limitations
Resources¶
[Metrics API Reference]({{ github_org_url }}/{{ book_repo }}/wiki/metrics-api)
Validation Datasets
Future Developments¶
Planned enhancements:
Automated model selection based on evaluation metrics
Multi-model ensembling with performance-weighted aggregation
Continual learning with online evaluation
Explainable AI techniques for model interpretability