Forest Fire Recognition and Prediction Based on Fully Convolutional Network and Rothermel Model

Abstract

This paper establish a combined fire recognition and prediction model to study the spread of forest fires in response to the frequent occurrence of forest hill fires and its difficult recognition and prediction pain point. Based on the traditional recognition neural network, this paper innovatively establishes Fully Convolutional Network (FCN) to improve the fire recognition accuracy. The Rothermel model is introduced for fire spread prediction of the Palisades Mountain Fire in Los Angeles, and it is found that the accuracy of the Rothermel model is as high as 87% and the stability is about 70.9%. Referring to the excellent model performance of the Rothermel model, this paper establishes a combined model for identification and prediction with the combination of FCN and Rothermel in order to improve the accuracy of fire identification and prediction, and provide double accuracy to ensure the reliability of the study. Based on the simulation of forest fires in a simulated wildland environment, the fire spreading stages are segmented into 4 parts, fire recognition is performed by FCN network, and Rothermel fire prediction is performed based on the recognition results. It is found that the combined model effectively reduces the errors of individual models, complements the advantages of individual models, and improves the fire identification and prediction accuracy. At last, this paper suggests a combination with the field of drones for smart fire prevention and reference.