forest_fires: Forest Fires
In tyluRp/ucimlr: UCI Machine Learning Repository

Description Usage Format Details Source References

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.

1	forest_fires

A data frame with 517 observations on the following 13 variables.

x: x-axis spatial coordinate within the Montesinho park map: 1 to 9
y: y-axis spatial coordinate within the Montesinho park map: 2 to 9
month: month of the year: "jan" to "dec"
day: day of the week: "mon" to "sun"
ffmc: FFMC index from the FWI system: 18.7 to 96.20
dmc: DMC index from the FWI system: 1.1 to 291.3
dc: DC index from the FWI system: 7.9 to 860.6
isi: ISI index from the FWI system: 0.0 to 56.10
temp: temperature in Celsius degrees: 2.2 to 33.30
rh: relative humidity in
wind: wind speed in km/h: 0.40 to 9.40
rain: outside rain in mm/m2 : 0.0 to 6.4
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).

This is a very difficult regression task. It can be used to test regression methods. Also, it could be used to test outlier detection methods, since it is not clear how many outliers are there. Yet, the number of examples of fires with a large burned area is very small.

Note: several of the attributes may be correlated, thus it makes sense to apply some sort of feature selection.

Past usage: P. Cortez and A. Morais. A Data Mining Approach to Predict Forest Fires using Meteorological Data. In Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, December, 2007. (http://www.dsi.uminho.pt/~pcortez/fires.pdf)

In the above reference, 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 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.

Created by: Paulo Cortez and Anibal Morais (Univ. Minho) @ 2007

P. Cortez and A. Morais. A Data Mining Approach to Predict Forest Fires using Meteorological Data. In J. Neves, M. F. Santos and J. Machado Eds., New Trends in Artificial Intelligence, Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, December, Guimaraes, Portugal, pp. 512-523, 2007. APPIA, ISBN-13 978-989-95618-0-9. Available at: http://www.dsi.uminho.pt/~pcortez/fires.pdf

https://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/

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

tyluRp/ucimlr documentation built on Feb. 2, 2021, 6:51 a.m.