Research On Emergency Management And Resilience Construction Of Urban Community Based On Machine Learning

With the increasing diversification and complexity of urban disasters,community as the basic receptor of urban disaster impact,emergency management and resilience construction for urban community has gradually become the key work of urban disaster prevention and reduction.However,there are some problems in the emergency management and resilience construction of urban community in our country,such as the evaluation system is too macroscopic,the measurement method is influenced subjectively,and the interaction between the two systems is not clear.Therefore,in this study,machine learning algorithm is introduced to measure the emergency management and resilience construction of urban community,and a quantitative measurement model is constructed.The main contents include:(1)Through the carding,summary and analysis of the existing research at home and abroad,it is established that the main goal of this study is the measurement of urban community emergency management and resilience construction.Around the main objectives established in this study,this paper defines the related concepts of urban community,emergency management and community resilience,and makes a preliminary study on the relationship between urban community emergency management and resilience construction based on relevant theories.On the basis of summarizing the high-frequency disasters in the community,the characteristics and mechanism of resilience are sorted out,which lays a foundation for the construction of the following index system and quantitative measurement.(2)The NLP text processing method of machine learning algorithm is used to extract the adaptive text and establish the database of the existing literature research and theory,and the(VSM)matching operation of the space vector model is carried out between the database and the relevant Chinese policy standards,which realizes the text extraction of the index system which adapts to the development of our country.Input the extracted data into the improved BP-RBF neural network model based on ant colony algorithm,use the MIV algorithm to further filter and purify the extracted text data,and then select the statistical data of Beijing as samples,combined with the information entropy algorithm to determine the number of nodes at all levels of the index system and construct the mapping relationship between the indicators,and output the index system that adapts to the current development situation of Beijing.Finally,combined with random forest-entropy weight method for index weighting,the accuracy of the weighting model is 94.332%,which proves that the system and weight setting are scientific and reasonable.(3)Based on the regression analysis and dynamic analysis of the panel data in Beijing,the coupling coordination model between urban community emergency management and resilience construction is constructed,and the coupling coordination degree of each region is analyzed and evaluated.and the representative case community is selected according to the evaluation results.The neural network model based on deep learning is used to measure the case community,and the targeted construction strategy is put forward combined with the horizontal and vertical analysis of the measurement results.In this research,intelligent algorithms related to machine learning are used to empower urban community emergency management and resilience construction measurement and strategy research,and a strong pertinence index system is constructed.On the basis of defining the coupling and coordination relationship between the two systems,the adaptability quantitative measurement method and representative cases are selected to ensure the accuracy of the quantitative measurement process and the feasibility of the construction strategy,to provide data ideas and decision-making reference for the development of urban community emergency management and resilience construction.