Research on fire prediction model based on improved Harris hawk optimization algorithm

Abstract

Accurate prediction of fire environment changes is helpful to accurately grasp the development trend of fire and ensure the safety of personnel. It is difficult to establish an accurate prediction model because of the coexistence of multiple parameters, complex coupling relationship, time series and nonlinearity of fire environment. In this paper, long shortterm memory network model (LSTM) based on improved Harris Hawk algorithm (CHHO) is proposed to achieve accurate prediction of fire environment data. Then, CHHO is used to optimize the hyperparameters in LSTM, and the fire temperature is predicted based on the optimized parameters. The experimental results show that the method of CHHO automatic parameter selection solves the problem of manual selection of LSTM model parameters and gives full play to the best performance of the model. The five environmental parameters of indoor fire temperature was predicted. The average fitting effect of CHHO-LSTM reached 94 %. The results show that the model has high prediction accuracy.