RA

RAVENSAI

Eden Climate Intelligence

Eden System

Architecture designed for traceable climate intelligence

Eden follows a layered architecture that preserves source provenance and model transparency from ingestion to advisory output.

LAYER 1

Data Ingestion

  • CHIRPS precipitation feeds
  • NASA POWER climate variables
  • FAOSTAT crop and production baselines
  • Sentinel-derived vegetation indicators
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LAYER 2

Data Processing Layer

  • Temporal alignment to dekadal intervals
  • Outlier filtering and missing-value imputation
  • Geospatial tiling at district resolution
  • Feature store with reproducible snapshots
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LAYER 3

AI Models

  • LSTM for seasonal rainfall sequences
  • Random Forest for nonlinear crop response
  • XGBoost for calibrated risk classification
  • Ensemble weighting by validation performance
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LAYER 4

Decision Intelligence Engine

  • Rule engine for advisory thresholds
  • Confidence-aware recommendations
  • Automated alert prioritization
  • Partner-ready API and reporting output

Model Layer

AI models used in operational forecasting

Eden combines sequence learning and tree-based methods so outputs remain accurate across different data densities and climate regimes.

LSTM

Captures temporal rainfall dynamics and delayed climate effects.

  • - Robust across multi-season sequence data
  • - Effective for planting-window timing
  • - Handles trend shifts when retrained on recent seasons

PRIMARY OUTPUTPlanting window probability bands

Random Forest

Models nonlinear interactions between weather, soil, and crop signals.

  • - Interpretable feature importance by district
  • - Stable performance in data-sparse contexts
  • - Strong baseline for yield band prediction

PRIMARY OUTPUTDistrict-level yield class estimates

XGBoost

Delivers calibrated risk scoring for drought and extreme-weather alerts.

  • - High precision in binary and multi-class risk tasks
  • - Fast retraining for new data cycles
  • - Supports SHAP-based explanation workflows

PRIMARY OUTPUTClimate risk alert severity scores

Decision Intelligence Engine

From predictions to action-ready recommendations

The final layer encodes institutional policies and operational thresholds so outputs are immediately usable for intervention planning.

Recommendation Logic

  • - Threshold rules map forecast distributions to advisory categories.
  • - Confidence-aware fallback logic avoids overconfident decisions in sparse data conditions.
  • - Alert ranking prioritizes districts by expected impact severity.

Delivery Interfaces

  • - Structured JSON/API outputs for institutional dashboards.
  • - Bulletin-ready narrative summaries for extension teams.
  • - Audit-friendly metadata for donor and research reporting.