algerian_forest_fires

This notebook applies a neural network to identify whether certain conditions led to a forest fire on a given day. Accuracy as of August 18, 2021: 0.671

Dataset provided by Faroudja ABID et al. , Predicting Forest Fire in Algeria using Data Mining Techniques: Case Study of the Decision Tree Algorithms, International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD 2019) , 08 - 11 July , 2019, Marrakech, Morocco.

URL: https://archive.ics.uci.edu/ml/datasets/Algerian+Forest+Fires+Dataset++

Attribute Information:

1. Date : (DD/MM/YYYY) Day, month ('june' to 'september'), year (2012) Weather data observations
2. Temp : temperature noon (temperature max) in Celsius degrees: 22 to 42
3. RH : Relative Humidity in %: 21 to 90
4. Ws : Wind speed in km/h: 6 to 29
5. Rain: total day in mm: 0 to 16.8 FWI Components
6. Fine Fuel Moisture Code (FFMC) index from the FWI system: 28.6 to 92.5
7. Duff Moisture Code (DMC) index from the FWI system: 1.1 to 65.9
8. Drought Code (DC) index from the FWI system: 7 to 220.4
9. Initial Spread Index (ISI) index from the FWI system: 0 to 18.5
10. Buildup Index (BUI) index from the FWI system: 1.1 to 68
11. Fire Weather Index (FWI) Index: 0 to 31.1
12. Classes: two classes, namely Fire and Not Fire