Predicting and detecting fires on multispectral images using machine learning methods
Aitimov M. Kaldarova M. Kassymova A. Makulov K. Muratkhan R. Nurakynov S. Sydyk N. Bapiyev I.
2024Institute of Advanced Engineering and Science
International Journal of Electrical and Computer Engineering
2024#14Issue 21842 - 1850 pp.
In todays 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.
Extreme gradient boosting , Fire , Logistic regression , Machine learning , Multispectral images , network , Vanilla convolutional neural
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Kyzylorda Regional Branch, The Academy of Public Administration Under The President of The Republic of Kazakhstan, Kyzylorda, Kazakhstan
Department of Information Systems, S. Seifullin Кazakh Research Agrotechnical University, Astana, Kazakhstan
Department of Information Technology, Faculty of Technology, Zhangir Khan University, Uralsk, Kazakhstan
Department of Computer Science, Faculty of Science and Technology, Caspian University of Technology and Engineering Named after Sh. Yessenov, Aktau, Kazakhstan
Department of Applied Mathematics, Informatica of Karaganda Buketov University, Karaganda, Kazakhstan
Institute of Ionosphere, Almaty, Kazakhstan
Kyzylorda Regional Branch
Department of Information Systems
Department of Information Technology
Department of Computer Science
Department of Applied Mathematics
Institute of Ionosphere
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