{
  "@context": "https://schema.org",
  "@type": "Dataset",
  "name": "AgWaterAI Model Performance Metrics",
  "description": "Walk-forward validated performance metrics for AgWaterAI's production ML models used in California agricultural irrigation.",
  "creator": {
    "@type": "Organization",
    "name": "AgWaterAI",
    "url": "https://agwaterai.com"
  },
  "dateModified": "2026-02-11",
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "keywords": ["AI models", "walk-forward validation", "irrigation AI", "machine learning", "agriculture"],
  "data": {
    "validation_method": "Walk-forward validation (train on past, test on unseen future data)",
    "training_data": {
      "sensor_readings": "11M+",
      "pump_records": "3.9M+",
      "swp_ground_truth": "thousands",
      "years_of_data": "9+",
      "monitored_sites": 84,
      "active_acres": "14,000+"
    },
    "models": [
      {
        "name": "SGMA",
        "type": "Regression",
        "metric": "R-squared",
        "score": 0.9235,
        "purpose": "Water usage prediction for SGMA compliance",
        "description": "Predicts daily water consumption per field to help farmers stay within groundwater allocations"
      },
      {
        "name": "SENTRY",
        "type": "Classification",
        "metric": "AUC",
        "score": 0.8913,
        "purpose": "Plant stress detection 24-48 hours early",
        "description": "Detects water stress zones before visible symptoms using multi-depth soil moisture patterns"
      },
      {
        "name": "WATCHDOG",
        "type": "Classification",
        "metric": "AUC",
        "score": 0.838,
        "purpose": "Depth profile stress analysis",
        "description": "Analyzes soil moisture at 12 depths to identify root zone stress patterns"
      },
      {
        "name": "SENTRY-FORECAST",
        "type": "Classification",
        "metric": "AUC",
        "score": 0.8714,
        "purpose": "7-day ahead stress prediction",
        "description": "Predicts plant water stress 7 days in advance using weather forecasts and sensor trends, retaining 97.9% of same-day accuracy"
      },
      {
        "name": "Salt Flush",
        "type": "Classification",
        "metric": "Direction Accuracy",
        "score": 0.819,
        "purpose": "Post-irrigation salt movement prediction",
        "description": "Predicts whether irrigation will flush salts from root zone or cause accumulation, optimizing leaching schedules"
      },
      {
        "name": "Salt Lock",
        "type": "Regression",
        "metric": "Pearson r",
        "score": 0.764,
        "purpose": "Salinity stress prediction",
        "description": "Predicts soil salinity buildup risk from irrigation water quality"
      }
    ],
    "active_sites": 34,
    "total_historical_sites": 84,
    "live_demo": null
  }
}