基于Spark框架的森林防火大数据分析

Big Data Analysis on Forest Fire Prevention Based on the Apache Spark

本研究利用Spark框架开源森林火灾数据集进行森林防火建模分析,对数据进行预处理,包括数据探索、缺失值处理和特征相关性分析,并采用线性回归、决策树和随机森林三种机器学习算法进行模型构建,对模型性能进行评估。结果显示,随机森林模型在预测森林火灾方面表现出卓越性能,同时揭示了气象数据、地理信息等特征对森林防火的重要影响。针对此,本文提供了一个实施方案,为该方法在大数据背景下的有效分析和应用提供了实践支持,以期为森林防火领域的未来研究和应用提供参考。

In this paper,an analysis was made on the forest five prevention modeling with Apache Spark open-source forest fires data set.The data was pre-processed including data exploration,handling of missing values and feature correlation analysis.Three machine learning algorithms,namely linear regression,decision-making tree and random forest were adopted for the model building and an assessment was made on the model performance.The results showed that the random forest model exhibited excellent performance in predicting forest fires and highlighted the significant influence of meteorological data and geographical information on forest fire prevention.A targeted implementation plan was put forward to provide practical support for effective analysis and application of this method in the context of big data as well as reference for future research and application in forest fire prevention.