Optimizing the Rothermel model for easily Predicting spread rate of forest fire

Jun Hua, Shiyu Zhang, Hewei Gao, Xiandong Chen, Xingdong Li, Jiuqing Liu


Rothermel model is a common method for predicting forest fire spread rate, but Its application is limited, due to complexity of the formula and too many parameters. In this paper, the Rothermel model is optimized to a simple format, which contains 4 independent variables as input, 1 dependent variable as output and 8 parameters to be estimated. In order to validate the effectiveness of the optimized model, the indoor ignition experiment is designed and carried out, and then the fire spreading data is collected and processed in advance for training the parameters of the model. By analyzing the effectiveness of 3 nonlinear optimizing methods , the Levenberg-Marquardt(LM) method is chosen to estimated the parameters of the model. At last, by comparing to the actual measured value, precision of the optimized model is validated on the verification data, and with the ability to predict the speed of fire spreading in the indoor laboratory.


Rothermel forest fire spread model; fire behavior; nonlinear fitting; prediction of spread rate

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