earticle

영어

Predicting fire nature is artistry as much as it’s a science. Forecasting the burnt area and range of field plays a vital role in resource abatement and renewal efforts. Literature studies have shown that machine learning techniques achieved better performance in forecasting and trend perusal. The purpose of this paper is to investigate the relevance of the state-of-the-art machine learning techniques epsilon Support Vector Regression and Nu-SVR to predict forest fire occurrence and burned area utilizing the meteorological data. The goals of this research are to (1) Identifying the best parameter settings using a grid-search and pattern search technique; (2) comparing the prediction accuracy among the models using different data sorting methods, random sampling and cross-validation. In conclusion, the experiments show that E-SVR performs better using various fitness-functions and variance analysis. The study is carried out to build predictive models for guesstimating the risk of the outbreaks in Montesinho Natural Park.