Earticle

초록

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.

목차

Abstract
 1. Introduction
 2. Literature Review
 3. Predictive Model
  3.1. Data Set Resource
 4. Experiments and Results
 5. Conclusion
 References

키워드

저자

• Somya Jain [ Division of Computer Engineering Netaji Subas Institute of Technology, New Delhi University of Delhi ]
• MPS Bhatia [ Division of Computer Engineering Netaji Subas Institute of Technology, New Delhi University of Delhi ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605