Operational Deployment: Evaluation Framework and Model Improvements

[sandrohuni]

Summary

This PR introduces a comprehensive operational evaluation framework and several critical improvements to the monthly discharge forecasting system. The changes focus on operational deployment readiness, model uncertainty quantification, and enhanced evaluation capabilities.

Major Changes

🔧 Operational Evaluation Infrastructure (scripts/evaluate_operational.py, scripts/examine_operational_fc.py)

• Added complete operational evaluation pipeline (~1,900 lines) for production model assessment
• Implemented forecast examination tools for operational outputs
• Enables systematic evaluation of models in production settings with proper temporal validation

🧪 MC-ALD Investigation (scripts/investigate_mcald.py)

• Created dedicated analysis script (~1,000 lines) for Monte Carlo-Asymetric Laplace Distribution
• Enables deep investigation of uncertainty quantification behavior
• Supports debugging and optimization of probabilistic forecasting components

🎯 Model Enhancements

• Uncertainty Mixture Model (lt_forecasting/forecast_models/deep_models/uncertainty_mixture.py):

  • Major refactoring of mixture-based uncertainty quantification (107 lines changed)
  • Improved handling of probabilistic predictions
  • Better integration with operational workflows

• Linear Regression (lt_forecasting/forecast_models/LINEAR_REGRESSION.py):

  • Enhanced feature selection with K-best approach (26 lines changed)
  • Better baseline model performance

🔍 Feature Processing Improvements

• Updated FeatureExtractor.py and FeatureProcessingArtifacts.py
  • Enhanced handling of temporal features
  • Improved GlacierMapper feature integration
  • Better normalization and preprocessing

📊 Visualization Enhancements (dev_tools/visualization/gla_eval.py)

• Agricultural season filtering (April-September focus)
• Improved color schemes for model comparison
• Enhanced monthly performance plots with better categorical handling
• Feature importance analysis tools

📚 Documentation Updates

• Updated README.md with clearer model family descriptions
• Refined workflow diagram (docs/Monthly_Workflow_updated.drawio)
• Removed placeholder sections for cleaner documentation

Key Improvements

1. Production Readiness: Complete evaluation framework for operational deployment
2. Uncertainty Quantification: Enhanced MC-ALD implementation and analysis tools
3. Feature Engineering: Better handling of GlacierMapper and temporal features
4. Evaluation Tools: Comprehensive scripts for model performance assessment
5. Code Quality: Focused refactoring of core model components

Technical Details

• Total Files Changed: 10 files
• Lines Added: ~5,000+ (mostly new evaluation infrastructure)
• Lines Modified: ~200 (model improvements and refactoring)

Testing Strategy

• Run operational evaluation pipeline on production data
• Verify MC-ALD uncertainty estimates are calibrated
• Validate GlacierMapper feature integration
• Check agricultural season filtering produces correct results
• Ensure backward compatibility with existing trained models

Review Focus

• Operational Scripts: Verify evaluation logic properly handles temporal splits
• Uncertainty Model: Check mixture model improvements maintain calibration
• Feature Processing: Ensure GlacierMapper features integrate correctly
• Visualization: Confirm agricultural season filtering works as expected

🤖 Generated with Claude Code

Co-Authored-By: Claude noreply@anthropic.com