Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

Algerian Forest Fires Dataset Data Set
Download: Data Folder, Data Set Description

Abstract: The dataset includes 244 instances that regroup a data of two regions of Algeria.

Data Set Characteristics:  

Multivariate

Number of Instances:

244

Area:

Life

Attribute Characteristics:

Real

Number of Attributes:

12

Date Donated

2019-10-22

Associated Tasks:

Classification, Regression

Missing Values?

N/A

Number of Web Hits:

25256


Source:

Faroudja ABID,fabid '@' cdta.dz, abidfaroudja '@' gmail.com, Microelectronic & Nanotechnology Division, Center for Development of Advanced Technologies (CDTA).


Data Set Information:

The dataset includes 244 instances that regroup a data of two regions of Algeria,namely the Bejaia region located in the northeast of Algeria and the Sidi Bel-abbes region located in the northwest of Algeria.

122 instances for each region.

The period from June 2012 to September 2012.
The dataset includes 11 attribues and 1 output attribue (class)
The 244 instances have been classified into ‘fire’ (138 classes) and ‘not fire’ (106 classes) classes.


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”


Relevant Papers:

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



Citation Request:

Please, include this citation when using this dataset.
Faroudja ABID et al. , “Predicting Forest Fire in Algeria using Data Mining Techniques: Case Study of the Decision Tree Algorithm”, International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD 2019) , 08 - 11 July , 2019, Marrakech, Morocco.


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML