@ARTICLE{26583204_243258676_2018, 
	author = {Tatiana Stankevich}, 
	keywords = {, forest fire, database, visual data, artificial intelligence, deep machine learning, convolutional neural network, Big Datareal-time forecasting},
	title = {The use of convolutional neural networks to forecast the dynamics of spreading forest fires in real time},
	journal = {},
	year = {2018},
	number = {4 (46)},
	pages = {17-27},
	url = {https://bijournal.hse.ru/en/2018--4 (46)/243258676.html},
	publisher = {},
	abstract = {      This work focuses on the relevant task of increasing the efficiency of forecasting the dynamics of forest fires spreading in real time. To address the problem, it was proposed to develop a method for operational forecasting the forest fire spread dynamics in the context of unsteadiness and uncertainty based on some advanced information technologies, i.e. artificial intelligence and deep machine learning (the convolutional neural network). As part of the research, both domestic and foreign models for the spread of forest fires were evaluated, and the key limitations of using models in real fire conditions were identified (high degree of dynamism and uncertainty of input parameters, the need to ensure minimum collection time and input parameters, as well as minimum response time of the model). Based on the data obtained, the need to use artificial neural network tools to solve the problem of predicting the forest fire’s spread dynamics was substantiated. A general logic diagram of the method for forecasting the forest fire dynamics in real time has been developed, the main feature of which is the construction of a tree of convolutional neural networks. To enhance the quality of learning convolutional neural networks that implement the function of predicting the spread of forest fires, we propose to create a database of forest fire dynamics.This study was supported by the Russian Foundation for Basic Research, project No. 18-37-00035 "On the dependence of the dynamics of the development of forest fires on the influence of environmental factors, the nature of forest plantations and the type of fire under conditions of nonstationarity and uncertainty"},
	annote = {      This work focuses on the relevant task of increasing the efficiency of forecasting the dynamics of forest fires spreading in real time. To address the problem, it was proposed to develop a method for operational forecasting the forest fire spread dynamics in the context of unsteadiness and uncertainty based on some advanced information technologies, i.e. artificial intelligence and deep machine learning (the convolutional neural network). As part of the research, both domestic and foreign models for the spread of forest fires were evaluated, and the key limitations of using models in real fire conditions were identified (high degree of dynamism and uncertainty of input parameters, the need to ensure minimum collection time and input parameters, as well as minimum response time of the model). Based on the data obtained, the need to use artificial neural network tools to solve the problem of predicting the forest fire’s spread dynamics was substantiated. A general logic diagram of the method for forecasting the forest fire dynamics in real time has been developed, the main feature of which is the construction of a tree of convolutional neural networks. To enhance the quality of learning convolutional neural networks that implement the function of predicting the spread of forest fires, we propose to create a database of forest fire dynamics.This study was supported by the Russian Foundation for Basic Research, project No. 18-37-00035 "On the dependence of the dynamics of the development of forest fires on the influence of environmental factors, the nature of forest plantations and the type of fire under conditions of nonstationarity and uncertainty"}
}