Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS) — ##plugins.generic.mcfnsReadingTools.readingTools##

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Parameter Estimation And Data-Driven Method For Forest Fire Prediction

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1.Title##plugins.generic.mcfnsReadingTools.titleLabel##Parameter Estimation And Data-Driven Method For Forest Fire Prediction
2.Creator##plugins.generic.mcfnsReadingTools.creators##X. Li; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China; Forestry Artificial Intelligence and Equipment Engineering Technology Research Center of the State Forestry and Grassland Bureau, Northeast Forestry University, Harbin 150040, China; Cina
2.Creator##plugins.generic.mcfnsReadingTools.creators##C. Tang; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China; Cina
2.Creator##plugins.generic.mcfnsReadingTools.creators##H. Zhang; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China; Cina
2.Creator##plugins.generic.mcfnsReadingTools.creators##S. Zhang; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China; Cina
2.Creator##plugins.generic.mcfnsReadingTools.creators##S. Li; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China; Forestry Artificial Intelligence and Equipment Engineering Technology Research Center of the State Forestry and Grassland Bureau, Northeast Forestry University, Harbin 150040, China; Cina
2.Creator##plugins.generic.mcfnsReadingTools.creators##Y. Wang; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China; Forestry Artificial Intelligence and Equipment Engineering Technology Research Center of the State Forestry and Grassland Bureau, Northeast Forestry University, Harbin 150040, China; Cina
2.Creator##plugins.generic.mcfnsReadingTools.creators##S. Sun; College of Engineering and Technology, Northeast Forestry University, Harbin 150040, China; Cina
2.Creator##plugins.generic.mcfnsReadingTools.creators##J. Liu; College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China; Forestry Artificial Intelligence and Equipment Engineering Technology Research Center of the State Forestry and Grassland Bureau, Northeast Forestry University, Harbin 150040, China; Cina
3.Subject##plugins.generic.mcfnsReadingTools.keywords##parameter estimation; Grey wolf optimization; data-driven; fireline prediction
3.Subject##plugins.generic.mcfnsReadingTools.disciplines##Fire Management
4.Description##plugins.generic.mcfnsReadingTools.abstract##Improvement in the accuracy of the forest fire prediction model is essential to properly instructfirefighting forces. The input parameters of traditional prediction method cannot be adjusted in real-time,so the forecasting accuracy will decrease over time. To solve this problem, the forest fire predictionsystembased on parameter estimation and data-driven method is proposed in this paper. First, twodynamic parameters based on the empirical formula, rate of fire spread and main spreading direction,and multi-sensor data are input to a forward prediction model based on the Huygens principle togenerate the predicted fireline for the current time. Secondly, the difference between the predicted andobserved firelines is minimized by the Grey Wolf Optimization algorithm, which derives the optimaldynamic parameters.Finally, the optimal parameters and the current multi-sensor data are input into theprediction model to achieve accurate prediction of the fireline. The burn experiment was designed, andthe feasibilityof the systemwasverifiedbyreal fire data. The results indicate thata fire prediction systemthat quickly calibrates dynamic input parametersis developed and can achieve real-time accurate firepredictions.
5.Publisher##plugins.generic.mcfnsReadingTools.publisher##Contemporary Journal Concept Press
6.Contributor##plugins.generic.mcfnsReadingTools.contributors##
7.Date##plugins.generic.mcfnsReadingTools.date##2023-04-30
8.Type##plugins.generic.mcfnsReadingTools.type##Text.Serial.Journal; Mathematical Modeling
9.Format##plugins.generic.mcfnsReadingTools.format##PDF (inglese); application/pdf
10.Identifier##plugins.generic.mcfnsReadingTools.identifier##https://mcfns.com/index.php/Journal/it/article/view/15.2
11.Source##plugins.generic.mcfnsReadingTools.source##Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); V. 15 N. 1: MCFNS April 30, 2023; 8-17 (10)
11.Source##plugins.generic.mcfnsReadingTools.onlineIssn##1946-7664
12.Language##plugins.generic.mcfnsReadingTools.language##en
13.Relation##plugins.generic.mcfnsReadingTools.suppFiles##
14.Coverage##plugins.generic.mcfnsReadingTools.coverageGeo##
15.Rights##plugins.generic.mcfnsReadingTools.rights##Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS) 2023