Services / Yield, risk, and anomaly models on geodata
Machine Learning & Predictive Modeling
We build predictive models on environmental data and we publish the methods behind them. Our hybrid neural network approach predicted groundwater change across 900,000 monitoring wells at R² above 0.85. Our spectral classifier separates healthy, water-stressed, and nutrient-deficient plants at 92.3% accuracy from a handheld sensor.
Capabilities
What the work involves
Hybrid neural network modeling
Multi-layer perceptrons with genetic algorithm optimization, applied to groundwater and yield prediction over 450,000 km² study areas.
Spectral classification
Support vector machines, weighted k-NN, and ensemble methods on visible-near-infrared spectra. Field results in under 30 seconds at 85% lower cost than lab analysis.
Uncertainty quantification
Monte Carlo analysis and independent validation on every model we ship. We report error, not just fit.
Process-model integration
Crop models (DSSAT), statistical downscaling, and 33-year climate calibration for scenario forecasting.
Tooling
- Python
- Neural networks
- Genetic algorithms
- SVM and ensemble methods
- DSSAT
- Monte Carlo methods
Deliverables
What you receive
- Trained, validated model with documented error bounds
- Reproducible training pipeline and data lineage
- Prediction surfaces as GIS-ready layers
- Peer-review-grade methods documentation
Where it applies
Markets served by this service
Delivered work
This service in the field
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