基于机器学习对森林火灾的预测分析

Prediction and Analysis of Forest Fire Based on Machine Learning

森林火灾是一种破坏性及其巨大的灾难,在对生态环境造成难以挽回的破坏的同时还对人类生存与生活带来极大的危害,特别是20世纪80年代以来,全球气候持续变暖,林火有上升的趋势,每年发生的森林火灾都给世界各国造成了巨大的经济损失,使得对于如何预测、防治或减少森林火灾的危害成为许多学科领域共同关注的科学任务。而快速检测正是预测森林火灾的一个有效途径。为了实现这一目标,一种方法是使用基于传感器的自动工具,如气象观测站所提供的数据。研究发现,气象条件(如气温,风速)是影响森林火灾发生和一些火灾指标(如森林火险天气指数)的重要因素。因此,我们将探讨几种机器学习预测森林火灾面积的方法。利用来自葡萄牙东北部的Montesinho国家公园采集测试的真实数据,使用多种不同的机器学习技术,如支持向量机(SVM)和随机森林,对四组不同的特征(分布空间,时间,气候指标和FWI系统指标)进行分析。最好的结果是使用支持向量机和四个基本气象输入(如气温,相对湿度,风速和降水量),它能够准确预测规模较小且发生频繁的火灾的受灾面积。上述预测方法对于提高消防资源的管理和调配有重大意义。

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.