Fire origin prediction in single compartment based on BP neural network and soot deposition characteristics
In order to help fire investigators to determine the fire origin more accurately and efficiently,a BP neural network-based fire point prediction model is proposed in this paper.The soot de-position database of wall soot deposition under 59 different fire ori-gin scenarios is constructed by numerical simulation of single com-partment fire,and the wall soot deposition characteristics under representative fire origin scenarios are analyzed,which indicates a strong correlation of the fire origin location with the mass of wall deposition and the average value of the maximum concentration.The above two parameters are selected as input,and the fire ori-gin location is used as output for network training.And the new data is used for prediction.The results show that the maximum absolute error of the predicted value is 0.65 m,the minimum ab-solute error is 0.03 m,and the average absolute error is 0.37 m,indicating that the proposed model can achieve the prediction of fire source location with relatively high accuracy and is a good al-ternative method for fire investigation.
BP neural networksoot depositionnumerical simu-lationsingle compartmentfire origin
NETL
NSTL
万方数据
维普
