Forest Fires
Donated on 2/28/2008
This is a difficult regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data (see details at: http://www.dsi.uminho.pt/~pcortez/forestfires).
Dataset Characteristics
Multivariate
Subject Area
Physical Science
Associated Tasks
Regression
Feature Type
Real
# Instances
517
# Features
-
Dataset Information
Additional Information
In [Cortez and Morais, 2007], the output 'area' was first transformed with a ln(x+1) function. Then, several Data Mining methods were applied. After fitting the models, the outputs were post-processed with the inverse of the ln(x+1) transform. Four different input setups were used. The experiments were conducted using a 10-fold (cross-validation) x 30 runs. Two regression metrics were measured: MAD and RMSE. A Gaussian support vector machine (SVM) fed with only 4 direct weather conditions (temp, RH, wind and rain) obtained the best MAD value: 12.71 +- 0.01 (mean and confidence interval within 95% using a t-student distribution). The best RMSE was attained by the naive mean predictor. An analysis to the regression error curve (REC) shows that the SVM model predicts more examples within a lower admitted error. In effect, the SVM model predicts better small fires, which are the majority.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
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Additional Variable Information
For more information, read [Cortez and Morais, 2007]. 1. X - x-axis spatial coordinate within the Montesinho park map: 1 to 9 2. Y - y-axis spatial coordinate within the Montesinho park map: 2 to 9 3. month - month of the year: 'jan' to 'dec' 4. day - day of the week: 'mon' to 'sun' 5. FFMC - FFMC index from the FWI system: 18.7 to 96.20 6. DMC - DMC index from the FWI system: 1.1 to 291.3 7. DC - DC index from the FWI system: 7.9 to 860.6 8. ISI - ISI index from the FWI system: 0.0 to 56.10 9. temp - temperature in Celsius degrees: 2.2 to 33.30 10. RH - relative humidity in %: 15.0 to 100 11. wind - wind speed in km/h: 0.40 to 9.40 12. rain - outside rain in mm/m2 : 0.0 to 6.4 13. area - the burned area of the forest (in ha): 0.00 to 1090.84 (this output variable is very skewed towards 0.0, thus it may make sense to model with the logarithm transform).
Papers Citing this Dataset
By Vasanth Iyer, S. Iyengar, Niki Pissinou. 2015
Published in SIGAI.
By Yourim Yoon, Yong-Hyuk Kim, Alberto Moraglio, Byung Moon. 2011
Published in Inf. Sci..
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Cortez,Paulo and Morais,Anbal. (2008). Forest Fires. UCI Machine Learning Repository. https://doi.org/10.24432/C5D88D.
@misc{misc_forest_fires_162,
author = {Cortez,Paulo and Morais,Anbal},
title = {{Forest Fires}},
year = {2008},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5D88D}
}pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset forest_fires = fetch_ucirepo(id=162) # data (as pandas dataframes) X = forest_fires.data.features y = forest_fires.data.targets # metadata print(forest_fires.metadata) # variable information print(forest_fires.variables)
Creators
Paulo Cortez
Anbal Morais
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.