Performance Analysis of Support Vector Machine (SVM) on Challenging Datasets for Forest Fire Detection  

作　　者：Ankan Kar Nirjhar Nath Utpalraj Kemprai   Aman Ankan Kar;Nirjhar Nath;Utpalraj Kemprai;  Aman(Department of Computer Science, Chennai Mathematical Institute, Chennai, India;Department of Mathematics & Computer Science, Chennai Mathematical Institute, Chennai, India;Department of Data Science, Chennai Mathematical Institute, Chennai, India)

机构地区：[1]Department of Computer Science, Chennai Mathematical Institute, Chennai, India [2]Department of Mathematics & Computer Science, Chennai Mathematical Institute, Chennai, India [3]Department of Data Science, Chennai Mathematical Institute, Chennai, India

出　　处：《International Journal of Communications, Network and System Sciences》2024年第2期11-29,共19页通讯、网络与系统学国际期刊（英文）

摘　　要：This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.

关 键 词：Support Vector Machine Challenging Datasets Forest Fire Detection CLASSIFICATION 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]