Prediction and Analysis of Forest Fire Based on Machine Learning
作者: 刘丹 ：云南财经大学统计与数学学院，云南 昆明;
Abstract: Forest fire is a kind of destructive and huge disaster, which causes irreparable damage in the eco-logical environment and brings great harm to human survival and life. Especially since the 1980s, the global warming has continued, and forest fires occur more frequently, leading to huge economic losses to the world each year. So how to predict, prevent or reduce the hazards of forest fires become the common concern of many science disciplines. Rapid detection is an effective way to predict forest fire. To achieve this goal, one approach is to use automated tools based on sensor data, such as the data that meteorological stations offer. The study found that the meteorological conditions (such as temperature, wind speed) are important factors influencing forest fires and some fire indicators (such as forest fire weather index). Therefore, we will explore several machine learning methods to predict forest fire area. Using the data collected from Montesinho National Park in Northeastern Portugal, and a variety of different machine learning techniques, such as support vector machines (SVM) and random forests, four different characteristics (distribution of space, time, climate indicators and FWI system indicator) were analyzed. The best results were obtained using support vector machines and four basic meteorological inputs (such as temperature, relative humidity, wind speed and precipitation), which could accurately predict the damage area of small-scale and frequent fires. The above prediction methods are of great significance for improving the management and allocation of fire-fighting resources.
文章引用: 刘丹 (2016) 基于机器学习对森林火灾的预测分析。 统计学与应用， 5， 163-171. doi: 10.12677/SA.2016.52016
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