WILDFIRE CLASSIFICATION WITH DEEP LEARNING MODEL

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dc.contributor.authorTankue, Puwanai
dc.contributor.authorKruekaew, Boonhatai
dc.contributor.authorKimpan, Warangkhana
dc.date.accessioned2026-08-06T10:56:02Z
dc.date.available2026-08-06T10:56:02Z
dc.date.issued2026-07-01
dc.description.abstractThis research aims to develop models for wildfire classification from images. The objective is to provide decision-making support for wildfire control planning, prevention and management. Convolutional Neural Networks techniques are used to analyze a dataset consisting of three image groups: no fire images, fire images that are not wildfires, and wildfire images. The experimental results compared ResNet group, DenseNet group, MobileNet group, and EfficientNet group. The research findings indicate the best-performing model in this study is ResNet152V2, which achieved an accuracy of 92.75%. Furthermore, Precision, Recall, and F1-Score are within a satisfactory range. A web application has also been developed to facilitate users to detect and classify wildfire more conveniently.
dc.identifier.citationIcic Express Letters Part B Applications, 17(7), 669-677, 2026
dc.identifier.doi10.24507/icicelb.17.07.669
dc.identifier.issn21852766
dc.identifier.other2-s2.0-105041006136
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18238
dc.sourceIcic Express Letters Part B Applications
dc.subjectConvolutional Neural Networks
dc.subjectDeep learning model
dc.subjectNatural disasters
dc.subjectWildfire images classification
dc.titleWILDFIRE CLASSIFICATION WITH DEEP LEARNING MODEL
dc.typeArticle

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