Abstract: In order to further improve the accuracy of fire probability prediction, a GA-WOA-BP fire probability prediction model based on multi-feature parameters is proposed to solve the problems of low detection accuracy and poor generalization ability in the fitting process of BP neural network. Firstly, the fire characteristic parameters of beech and cotton rope smoldering and open burning were collected by experiment, and the corresponding probability of fire type was obtained after calculation. Secondly, by using genetic algorithm to optimize the hidden layer structure of BP neural network and whale optimization algorithm to optimize the initial weight of BP neural network, GA-WOA-BP model was constructed to improve the fitting ability of fusion algorithm. Finally, multi-characteristic fire parameters are used as model input and different types of fire occurrence probabilities are used as output to predict the fire probability. The results show that compared with the simple BP neural network, the model has better prediction performance, and its evaluation indexes RMSE, MAE and R2 are 0.020 22, 0.014 33 and 0.992 31, respectively, which can provide data reference for fire probability prediction.

Key words: multi-feature parameter, whale optimization algorithm, genetic algorithm, fire probability prediction, BP neural network