BP Neural Network for Typhoon Warning and Prevention for Historical Buildings

Typhoon is one of the most important natural disasters in the coastal areas of China and caused severe economic losses every year. A typhoon disaster early warning evaluation system for historical buildings is established, and the influence of risk factors is analyzed using the analytic hierarchy process. The corresponding prevention strategies are proposed to reference the disaster prevention and protection for historical buildings under typhoon. Theoretically, this paper uses the algorithm advantages of the BP neural network algorithm to achieve the purpose of early warning of typhoon disaster risk. It constructs a historical building typhoon warning model based on BP neural network to evaluate the risk level of historical buildings. The risk early warning model is found to have some validity and reliability by training the neural network with sample data and comparing the performance data with the predicted data.