Construction of a Risk Assessment Model for Urban Rainstorm Cascading Events Integrating Risk and Spatial Features
Compared with urban rainstorm hazardous events (such as waterlogging,flood,debris flow,etc.),existing studies pay less attention to the feature composition and objective assessment of risks associated with small-scale and diversified rainstorm cascading events (such as house damage,subway inundation,etc.),making is difficult to meet the goals of refined city management.At the same time,constructing risk assessment models for rainstorm cascading events faces constraints due to incomplete risk features in sample data.To address these issues,this paper proposes a risk assessment model for urban rainstorm cascading events that integrates risk features and spatial features,considering the spatial correlation between them.Firstly,for the risk scenarios of different rainstorm cascading events,the risk features are extracted from data sources such as grassroots officials'inspection,citizen reporting,and social media posts.Secondly,using the spatial localization of the original risk samples as a connection,an improved marginal Fisher method is employed to mine spatial features from multi-source spatial data to supplement the missing risk features.Finally,using a machine learning approach,the relationship between risk features and risk categories is established,leading to the construction of a risk assessment model for multi-category rainstorm cascading events.Experimental results from Wuhan,Hubei Province,China,show that the proposed method effectively addresses the problem of incomplete features in the construction of risk feature models through multi-source spatial feature mining,enabling the construction of diversified rainstorm cascading event risk assessment models.The overall accuracy,F1-score and AUC increased by 23％,24％,and 25％,respectively.Additionally,the complexity and diversity of spatial features highlighted the risks of subjective and arbitrary feature fusion,which can negatively affect the performance of machine learning model construction by adding irrelevant features.The proposed method mitigates this issue with an adaptive feature selection approach.Furthermore,grassroots officials'inspection records contributed the most to the construction of urban rainstorm cascading event risk assessment models,followed by citizen-reported texts,and finally,social media data.Compared to traditional disaster event risk assessment methods,urban rainstorm cascading event risks have smaller risk granularity and involve more complex and diverse risk types and features.Traditional comprehensive evaluation models face challenges of subjectivity in manual evaluation,while traditional disaster loss curve methods encounter high experimental costs and data scarcity.The method proposed in this paper utilizes objective data to generate multidimensional risk features and establishes relationships between diverse risk levels,resulting in a machine learning-based risk prediction model that is more suitable for small-scale risk assessment scenarios.
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