Urban storm flood disaster risk assessment based on social media data—A case study of the"7·20"rainstorm event in Zhengzhou City
In recent years,the increasing occurrences of urban flood disasters triggered by heavy rainfall have severe-ly endangered people's lives,health,and property safety.Objective and accurate urban flood disaster risk assessment is crucial for effectively enhancing disaster prevention and reduction capabilities.However,the lack and lag of basic data for urban disaster points restrict the accuracy of urban storm flood disaster risk assessment results.With the development of mobile internet technology,the relevant disaster information posted by the public on social media gradually accumulates into a massive,timely,and thematically clear resource known as social media data.Introdu-cing this resource into urban storm flood disaster risk assessment work undoubtedly holds significant importance in accurately depicting the overall picture of urban storm flood disasters.Taking the"7·20"rainstorm event in Zhengzhou City in 2021 as an example,this study first selected thirteen influencing factors from meteorological fac-tors,basic geographic information,and socio-economic factors.Then,leveraging web crawling technology,it ob-tained information on waterlogging points from Weibo data.Finally,using four machine learning models,namely GBDT,XGB,RF,and AdaB,the study conducted a risk assessment of the rainstorm flood disaster in Zhengzhou"7·20".The results are as follows:① The four sets of indicator weights obtained based on the above models are statistically consistent.Among the influencing factors,road density,vegetation coverage index,maximum rainfall in half an hour,and maximum daily rainfall all rank in the top five in terms of importance in the four sets of indicator importance rankings,indicating that these factors are the main causes of the rainstorm flood disaster;② Based on the Pearson correlation coefficient test,it is found that the correlation between the evaluation results of the four models is relatively high.The areas with extremely high risk are concentrated in the central parts of the five main urban areas of Zhengzhou,the northeast part of Zhongmu City,Micun Town and Chengguan Town in Xinmi City,and the surrounding areas of Gongyi Station in Gongyi City;③ The AUC and ACC values of the four models are all above 0.7,confirming the effectiveness of machine learning models in urban flood risk assessment.Compared with the GBDT,XGB,and RF models,the AdaB model has the highest accuracy,and the sum of the Rei values of the high-risk and extremely high-risk areas obtained by it is the largest,indicating that its evaluation results are consistent with the actual situation.By introducing social media data into urban storm flood disaster risk assessment work,this study effectively enhances the accuracy of the assessment results,providing decision-making basis for risk warning and emergency response to urban flood disasters in Zhengzhou City and similar cities under heavy rainfall events.
urban flood disasterrisk assessmentmachine learning modelsocial media data"7·20"rain-storm event in Zhengzhou City
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