Parameter Estimation And Data-Driven Method For Forest Fire Prediction
Ključne riječi:
parameter estimation, Grey wolf optimization, data-driven, fireline predictionSažetak
Improvement in the accuracy of the forest fire prediction model is essential to properly instruct firefighting 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 prediction system based on parameter estimation and data-driven method is proposed in this paper. First, two dynamic 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 to generate the predicted fireline for the current time. Secondly, the difference between the predicted and observed firelines is minimized by the Grey Wolf Optimization algorithm, which derives the optimal dynamic parameters. Finally, the optimal parameters and the current multi-sensor data are input into the prediction model to achieve accurate prediction of the fireline. The burn experiment was designed, and the feasibility of the system was verified by real fire data. The results indicate that a fire prediction system that quickly calibrates dynamic input parameters is developed and can achieve real-time accurate fire predictions.
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