Reasearch On Urban Fire Risk Assessment Model Based On Internet Search Query Data

With the promoting smart fire protection campaign,the accurate judgment and real-time monitoring of fire risk is the most important part to achieve fire prevention and control which is comprised of smart FireGuard and smart fire management.But we are facing a host of problems like the insufficient fire basic information,analysing the fire data by halves and the weak on time-effectiveness.It’s hardly to meet the demands of smart fire protection campaign.In 2008,Google found that some search queries have a high correlation with influenza patients.They developed Google Flu Trends(GFT)to indicate influenza activity.The GFT gained some notable achievements.The application of search query data provides a new way to improve timeliness of fire risk assessment.This thesis is about to apply search query data to fire risk assessment effectively and establish a fire risk assessment model based on search query data.It will improve timeliness of fire risk assessment and lay the foundation of deep learning application in fire risk assessment.The contents and results of this thesis are mainly as follows:(1)To begin with,this paper constructs a theoretical framework based on Heinrich’s theory of causality in accidents,Maslow’s hierarchy of needs and the theory of fire risk assessment.The theoretical framework reveals that public fire safety may motivate their demands for fire safety information,and further driving their fire safety information seeking behaviors.(2)From the aspects of fire safety knowledge,fire accident loss,fire rescue and fire safety awareness conduct primary selection and expansion of search keywords.30 keywords are selected in Indicator library,and 8 keywords are used to establish fire risk assessment index system after Pearson correlation coefficient analysis and co-integration test.(3)Four types of fire risk assessment model is established by four methods which are linear regression,principal component regression,vector auto-regression,and artificial neural network.The fitting degrees of the above four models are 0.744,0.659,0.683 and 0.712,respectively.However,in the selection of the model,not only the fitting degree but also the accuracy of the empirical test of the model should be considered.(4)After verification and analysis of the established models,it is found that the average relative error of the principal component regression model is 47.1%,which is better than that of the linear regression model(95.9%).By adding error correction formula,the average relative error of the vector auto-regression model was 40.9%.Compared with the above two models,the outliers in the samples were improved.By optimizing the parameters of the artificial neural network model,the fitting degree of the model is 0.95,much higher than the other three models,artificial neural network model of the average relative error is 25.3%,shows that it has the robustness and fault tolerance performance,better solve the multicollinearity between the indexes,for repetition of information between indexes have higher inclusive.Considering the learning and iteration ability of artificial neural network,it is suitable for building fire risk assessment model based on search query data.