[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123765-en":3,"doc-seo-123765-105":30,"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123765,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1786009248482753345",8,"Research & Report","Predicting and detecting fires on multispectral images using machine learning methods","Fire forecasting and early detection are crucial for limiting environmental damage and protecting settlements. This study develops and tests automated machine learning approaches that identify the initial stages of fires from multispectral image analysis. Three models are evaluated for processing and interpreting multispectral data: extreme gradient boosting (XGBoost), logistic regression, and a vanilla convolutional neural network (vanilla CNN). Results indicate that integrating these methods into monitoring systems can substantially improve early detection efficiency and support fire prediction.","Predicting and detecting fires on multispectral images using  \nmachine learning methods  \nMurat Aitimov1, Mira Kaldarova2, Akmaral Kassymova3, Kaiyrbek Makulov4, Raikhan Muratkhan5, Serik Nurakynov6, Nurmakhambet Sydyk6, Ideyat Bapiyev3  \n1Kyzylorda Regional Branch at the Academy of Public Administration under the President of the Republic of Kazakhstan, Kyzylorda,  \nRepublic of Kazakhstan  \n2Department of Information Systems, S. Seifullin Кazakh Research Agrotechnical University, Astana, Republic of Kazakhstan 3Department of Information Technology, Faculty of Technology, Zhangir Khan University, Uralsk, Republic of Kazakhstan 4Department of Computer Science, Faculty of Science and Technology, Caspian University of Technology and Engineering Named after  \nSh. Yessenov, Aktau, Republic of Kazakhstan  \n5Department of Applied mathematics and Informatica ofKaraganda Buketov University, Karaganda, Republic of Kazakhstan  \n6Institute of Ionosphere, Almaty, Republic of Kazakhstan  \nArticle history:  \nReceived Aug 27, 2023 Revised Oct 24, 2023 Accepted Nov 29, 2023  \nKeywords:  \nExtreme gradient boosting Fire  \nLogistic regression Machine learning Multispectral images Vanilla convolutional neural network  \nCorresponding Author:  \nIn today's world, fire forecasting and early detection play a critical role in preventing disasters and minimizing damage to the environment and human settlements. The main goal of the study is the development and testing of machine learning algorithms for automated detection of the initial stages of fires based on the analysis of multispectral images. Within the framework of this study, the capabilities of three popular machine learning methods: extreme gradient boosting, logistic regression, and vanilla convolutional neural network (vanilla CNN), are considered in the task of processing and interpreting multispectral images to predict and detect fires. XGBoost, as a gradient-boosted decision tree algorithm, provides high processing speed and accuracy, logistic regression stands out for its simplicity and interpretability, while vanilla CNN uses the power of deep learning to analyze spatial and spectral data. The results of the study show that the integration of these methods into monitoring systems can significantly improve the efficiency of early fire detection, as well as help in predicting potential fires.  \nThis is an open access article under the CC BY-SA license.  \nIdeyat Bapiyev  \nDepartment of Information Technology, Faculty of Technology, Zhangir Khan University 090000 Uralsk, Republic of Kazakhstan  \n[Email: bapiev@mail.ru](Email: bapiev@mail.ru)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nForest fires are one of the most destructive natural phenomena, causing significant damage to ecosystems, economies, and human well-being. In light of global climate change and increasing human impacts, the need for rapid and accurate fire prediction and detection has become increasingly urgent. The main challenge here is the need for effective fire prediction and detection in multispectral images. To solve this problem, it is proposed to develop and optimize highly efficient machine learning models capable of analyzing multispectral images to accurately predict and detect fires. These models must be trained on large amounts of data, including a variety of scenarios and types of multispectral data, to ensure their broad applicability. Traditional methods of detection and control often do not provide the necessary effectiveness, so the focus is on innovative approaches. In recent decades, the development of Earth remote sensing technologies has made it possible to obtain high-resolution multispectral images. These images [1]–[3]  \nprovide extensive information about the condition of the surface, including temperature, humidity, and other parameters that can serve as indicators of fire hazard. However, processing and analyzing such a volume of data requires the use of modern and effective tools. In this context, m","cbCaibWU5yrwCXzV","https://ap.wps.com/l/cbCaibWU5yrwCXzV","pdf",691630,1,9,"English","en",105,"# Introduction\n## Problem background and motivation\n## Related research and approaches\n# Methods\n## Selected machine learning models\n## Multispectral image processing for fire prediction","[{\"question\":\"What is the main goal of the study on fire detection?\",\"answer\":\"To develop and test machine learning algorithms that automatically detect the initial stages of fires by analyzing multispectral images.\"},{\"question\":\"Which machine learning methods are compared in the research?\",\"answer\":\"Extreme gradient boosting (XGBoost), logistic regression, and a vanilla convolutional neural network (vanilla CNN).\"},{\"question\":\"How do the proposed methods help in practical monitoring?\",\"answer\":\"Integrating them into monitoring systems improves the efficiency of early fire detection and supports predicting potential fires.\"}]","Predicting and detecting fires on multispectral images using machine learning methods | PDF",1785818410,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-and-detecting-fires-on-multispectral-images-using-machine-learning-methods","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-and-detecting-fires-on-multispectral-images-using-machine-learning-methods/123765/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study on fire detection?","Question",{"text":75,"@type":76},"To develop and test machine learning algorithms that automatically detect the initial stages of fires by analyzing multispectral images.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are compared in the research?",{"text":80,"@type":76},"Extreme gradient boosting (XGBoost), logistic regression, and a vanilla convolutional neural network (vanilla CNN).",{"name":82,"@type":73,"acceptedAnswer":83},"How do the proposed methods help in practical monitoring?",{"text":84,"@type":76},"Integrating them into monitoring systems improves the efficiency of early fire detection and supports predicting potential fires.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]