Algerian Wildfire Predictor
ML + Meteorology • Ridge Regression • StandardScaler

Predict Forest Fire Risk for Algerian Regions

A simple, fast Flask app powered by a trained Ridge Regression model. Enter meteorological features to estimate fire likelihood for Béjaïa and Sidi Bel‑Abbès (2012 season).

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Quick Facts

244
Instances
2
Regions
2012
Jun–Sep
11 + 1
Features + Target

Classes: Fire (138), Not Fire (106)

1. Input

Provide Temperature, RH, Wind, Rain and indices like FFMC, DMC, ISI—plus numeric encodings for Classes & Region.

2. Preprocess

Values are standardized with a fitted StandardScaler to match model training distribution.

3. Predict

A trained Ridge Regression model outputs the prediction, rendered instantly.

About the Dataset

  • Regions: Béjaïa (NE), Sidi Bel‑Abbès (NW)
  • Period: June–September 2012
  • 244 instances (122 per region)
  • 11 attributes + 1 target (Fire/Not Fire)

Tips

  • Use realistic ranges (e.g., Temp 15–45°C, RH 10–100%).
  • Keep decimals with a dot (e.g., 0.0 for Rain).
  • Classes & Region should match the numeric encoding from training.
Try the Predictor