[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118139-en":3,"doc-seo-118139-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118139,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Advancing Early Warning Systems for Fire Detection: A Comprehensive Approach in Machine Learning","This research investigates the effectiveness of multiple machine learning algorithms for fire detection, focusing on how well they identify and predict fires from relevant input features. Evaluated methods include logistic regression, decision tree, random forest, support vector classifier, gradient boosting, K-nearest neighbors, Gaussian naive Bayes, multilayer perceptron, and XGBoost. Experiments show logistic regression as the top performer, achieving 99% accuracy. Results support optimizing real-time fire monitoring systems by addressing limitations in existing detection precision. The study clarifies challenges, compares algorithm performance, and informs proactive responses to reduce wildfire impacts.","Research Article  \nAdvancing Early Warning Systems for Fire Detection: A Comprehensive Approach in Machine  \nLearning  \nAshwaq Katham Mtasher *1 Jenan Jader msad 2  \nInformation Technology Division Department of Computer Science  \nMedical and Health Technical College, KufaAl-Furat Al-Awsat Technical University  \nAl-Furat Al-Awsat Technical University Karbala, Iraq  \nKufa, Iraq [jenan.jader@atu.edu.iq](jenan.jader@atu.edu.iq)  \n[ashwaq.hafez.ckm@atu.edu.iq](ashwaq.hafez.ckm@atu.edu.iq)  \nDhakaa Mohsin Kareem3  \nDepartment of Accounting /Technical Institute Suwaira/Middle Technical University  \nSuwaira, Iraq  \n[Dhakaa.mohsin@mtu.edu.iq](Dhakaa.mohsin@mtu.edu.iq)  \nA R T I C L E I N F O  \nArticle History  \nReceived: 18/12/2023  \nAccepted: 29/1/2024  \nPublished: 30/6/2024 This is an open-access article under the CC BY 4.0 license:  \n[http://creativecommons](http://creativecommons). org/licenses/by/4.0/  \nABSTRACT  \nThis research conducts a comprehensive investigation of the efficacy of various machine learning algorithms for fire detection. The algorithms that were examined include logistic regression, decision tree, random forest, support vector classifier, gradient boosting, K-nearest neighbors, Gaussian naive Bayes, multilayer perceptron classifier, and XGBoost classifier. Through in-depth experiments, this study rigorously assesses the performance of these algorithms in identifying and predicting fires based on pertinent input features. Among the algorithms that were investigated, logistic regression is the best performer, with a high accuracy rate of 99% . The findings from this research offer valuable insights for optimizing fire detection systems, providing a nuanced understanding of the practical applicability of machine learning techniques in real-time fire monitoring scenarios. The primary objectives ofthis study are to elucidate specific challenges in fire detection, evaluate the performance of various machine learning algorithms, and contribute to the foundational knowledge that is essential for enhancing fire management strategies. The research addresses the limited precision of existing fire detection systems and aims to rectify this issue through a systematic exploration of advanced machine learning approaches. The overarching goal is to bolster the foundations of fire management, facilitating the development of proactive measures and prompt responses to mitigate the profound impact of wildfires. By presenting a detailed examination of the strengths and weaknesses of various machine learning algorithms, this research strives to foster a robust and effective approach to fire detection, thereby advancing the field and ensuring the safety of communities at risk.  \nKeywords: Decision Tree; Gradient Boosting; fire detection; SVC; Random Forest.  \n1. INTRODUCTION  \nForest fires pose a multifaceted threat, disrupting the ecological balance, endangering human safety, and causing irreparable damage to vast landscapes and populated areas. The increasing frequency and intensity of these fires are the result of dynamic shifts in climate patterns, urbanization, and intensified human activities. This escalation highlights the urgent need for innovative strategies to immediately detect and manage forest fires effectively. Addressing the improvement of machine learning frameworks for timely and accurate detection of wildfires can highlight the problem at hand. Existing systems have limited accuracy and efficiency. Thus, better methodologies need to be explored. This research is consistent with the work of Al-Khatib et al. (2023) [1], who conducted a brief review of machine learning algorithms in the context of wildfires. They emphasized the importance of artificial intelligence (Artificial intelligenceAI ) techniques, especially machine learning, in predicting and assessing forest fire risks. The ongoing problem of selecting the best prediction model has been noted, highlighting the need to explore different machine learning algori","cbCaicrpKDWJz0gR","https://ap.wps.com/l/cbCaicrpKDWJz0gR","pdf",745863,1,"English","en",105,"# Introduction\n# Related Work\n# Methods and Algorithms\n# Experimental Results and Evaluation\n# Conclusion","[{\"question\":\"Which machine learning algorithms were evaluated for fire detection?\",\"answer\":\"The study tested logistic regression, decision tree, random forest, support vector classifier, gradient boosting, K-nearest neighbors, Gaussian naive Bayes, multilayer perceptron classifier, and XGBoost classifier.\"},{\"question\":\"What algorithm performed best in the experiments?\",\"answer\":\"Logistic regression achieved the highest accuracy rate of 99% among the investigated algorithms.\"},{\"question\":\"What problem does the research aim to address in existing fire detection systems?\",\"answer\":\"The research targets the limited precision of current fire detection systems and seeks to improve accuracy and efficiency through advanced machine learning approaches for timely detection.\"}]","Advancing Early Warning Systems for Fire Detection: A Comprehensive Approach in Machine Learning | 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