In [Cortez and Morais, 2007], the output 'area' was initially transformed using a ln(x+1) function. Several Data Mining techniques were then applied to the data. After fitting the models, the outputs were post-processed with the inverse of the ln(x+1) transformation. Four different input configurations were utilized for the experiments, which were carried out using a 10-fold cross-validation with 30 runs. Two regression metrics, MAD and RMSE, were measured. A Gaussian support vector machine (SVM) using just 4 direct weather conditions (temperature, relative humidity, wind, and rain) achieved the best MAD value: 12.71 ± 0.01 (mean and confidence interval within 95% using a t-student distribution). The naive mean predictor attained the best RMSE. An analysis of the regression error curve (REC) indicated that the SVM model performed better in predicting more examples with a lower admitted error. Specifically, the SVM model was more effective at predicting smaller fires, which constitute the majority of the cases.
Forest Fires Dataset
Dataset Overview
Data Type | Multivariate | Default Task | Regression |
---|---|---|---|
Attribute Type | Real | Published Year | 2008 |
Area of Dataset | Physical | Missing Values | No |
No. of Instances | 517 | No. of Attribute | 13 |
Dataset Description:
Forest fires are a major environmental issue, creating economical and ecological damage while endangering human lives. Fast detection is a key element for controlling such phenomenon. To achieve this, one alternative is to use automatic tools based on local sensors, such as provided by meteorological stations. In effect, meteorological conditions (e.g. temperature, wind) are known to influence forest fires and several fire indexes, such as the forest Fire Weather Index (FWI), use such data. In this work, we explore a Data Mining (DM) approach to predict the burned area of forest fires.
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
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