Parameter Estimation And Data-Driven Method For Forest Fire Prediction

X. Li, C. Tang, H. Zhang, S. Zhang, S. Li, Y. Wang, S. Sun, J. Liu

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.


Keywords


parameter estimation; Grey wolf optimization; data-driven; fireline prediction

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References


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