Nidhi20-7/Algerian-Forest-fire-FWI-prediction

Domaine:

environment and energyclimate

Type de record:

dataset

Créateur:

Nid

Hôte:

# Algerian Forest fire FWI prediction Date : (DD/MM/YYYY) Day, month ('june' to 'september'), year (2012) Weather data observations Temp : temperature noon (temperature max) in Celsius degrees: 22 to 42 RH : Relative Humidity in %: 21 to 90 Ws :Wind speed in km/h: 6 to 29 Rain: total day in mm: 0 to 16.8 FWI Components Fine Fuel Moisture Code (FFMC) index from the FWI system: 28.6 to 92.5 Duff Moisture Code (DMC) index from the FWI system: 1.1 to 65.9 Initial Spread Index (ISI) index from the FWI system: 0 to 18.5 Fire Weather Index (FWI) Index: 0 to 31.1 Classes: two classes ```

SohamKanji/Algerian-Forest-Fire-FWI-Prediction

For more info on the data set : https://www.kaggle.com/datasets/nitinchoudhary012/algerian-forest-fi

ishivansmishra/algerian-forest-fire-fwi-prediction

forest-fire-fwi-prediction-ml fwi-prediction-flask-app algerian-forest-fire-ml-project forest-fire-w

kkb1110/Algerian-Forest-Fire-FWI-prediction-

Algerian forest fire (FWI) prediction using ridge regression. Ridge regression shows better accuracy

BetterCallEkangsh/Algerian-Forest-Fire-FWI-Prediction

This project analyzes the Algerian Forest Fires dataset — 244 weather observations from two regions

lavk7766/Algerian-Forest-Fire-FWI-Prediction

Mpss1701/Algerian-Forest-Fire-FWI-Prediction

End-to-end Machine Learning project on the Algerian Forest Fires dataset using Linear Regression, Ri