Rohini Patil, Janhvi Pawar, Kamal Shah, Disha Shetty, A. Ajith, Sakshi Jadhav
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引用次数: 0
摘要
野火是世界上最紧迫的问题之一,而且随着全球变暖和其他环境条件的恶化,野火越来越普遍。这些野火可能是人为的,也可能是自然的。野火是导致珍稀植物和野生动物灭绝的因素之一,而珍稀植物和野生动物正是维持地球生态平衡的重要力量。本文使用两个独立的数据集,对预测森林火灾的各种机器学习分类器模型进行了比较分析。建议系统的处理过程取决于温度、湿度、氧气和风力等一些特征。本研究使用了几种机器学习分类技术,包括逻辑回归、支持向量分类器、决策树、k 邻居和随机森林。为进一步优化模型,采用了 K 折交叉验证法和超参数调整法。系统显示,支持向量机是森林火灾数据集的最佳策略,准确率为 96.88%。随机森林方法是 Cortez 和 Morais 数据集的最佳方法,准确率为 90.24%。
Machine Learning based Forest Fire Prediction: A Comparative Approach
Wildfires are among the world's most pressing issues, and they are getting more prevalent as global warming and other environmental conditions deteriorate. These wildfires might be caused by humans or by natural causes. Wildfires are one of the factors contributing to the extinction of rare flora and wildlife that serve to maintain our planet's ecological balance. In this paper, a comparative analysis of various machine learning classifier models for predicting forest fires was undertaken using two separate datasets. The suggested system's processing is dependent on a few characteristics such as temperature, humidity, oxygen, and wind. Several machine learning classification techniques, including logistic regression, support vector classifier, decision tree, k neighbors and random forest, were used in this study. For further optimization of the model, K-fold cross validation method and hyperparameter tuning were implemented. The system reveals Support Vector Machine as the best strategy for the forest fire dataset, with 96.88% accuracy. Random Forest method was found to be the best for the Cortez and Morais dataset, with 90.24% accuracy.