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• This paper takes Beijing as a case to study regional economic resilience. • We systematically explore the effects of COVID-19 on industrial dynamics. • Individual-owned firms located in high-risk regions are more vulnerable. • The epidemic shock leads to cascading effects among the local industrial network. The COVID-19 epidemic has altered the economic geographies of cities. However, the academic focus on urban industries and their structural dynamics is far from enough. In this paper, we integrate literature from economic geography, business studies and network sciences, and design an adaptation framework to theorize urban economic resilience. Taking the COVID-19 outbreak in Xinfadi, Beijing during the summer of 2020 as a case study, we use Logit regressions and DID estimations to identify the impacts of the epidemic on local industrial dynamics. Our results support that the COVID-19 outbreak significantly increases the number of business exits. The negative impacts are concentrated in high-risk regions, pointing to specific industries and individual-owned firms. Furthermore, we use a machine learning method to visualize the dynamics of industry networks in a small-scale urban area, and then simulate the transmission paths of industry-failure. In doing so, we contribute to current economic resilience literature that firm-level adaptation and industrial dynamics complement regional adaptation theory, while the cascading effects in the industry network underpin the micro foundations of resilience formation. [ABSTRACT FROM AUTHOR]

• Experiments contrasted ten machine learning models in susceptibility prediction. • Hybrid ensemble models significantly outperformed widely-used individual models. • Study profiled the first urban waterlogging susceptibility map in Beijing, China. • Urban road network and densely populated areas face severer waterlogging risk. Urban waterlogging has emerged as a significant problem worldwide, particularly in densely populated cities. Accurate assessment of waterlogging susceptibility at the city scale is crucial for mitigating the risks associated with waterlogging and optimizing municipal design accordingly. However, existing studies on urban waterlogging susceptibility assessment have primarily relied on individual machine learning models. It is worthwhile to explore whether hybrid ensemble models have the potential to enhance the predictive performance. This research presents two hybrid ensemble machine learning models, namely Stacking and Blending, for assessing urban waterlogging susceptibility in the metropolitan area of Beijing, China. The performances of these models are compared with those of the widely used individual machine learning models. Evaluation of all the models is based on metrics such as Accuracy rate and Area Under Curve (AUC) score. The results demonstrate that the Stacking and Blending models consistently outperform the traditional machine learning models, such as Random Forest, Logistic Regression, etc. Through susceptibility analysis and model interpretation with SHAP method, this paper obtains several key findings that low lying areas may not necessarily be areas with severe waterlogging; urban roads and densely populated areas are highly susceptible to becoming high-risk areas for waterlogging in the study area. This study not only highlights the effectiveness of the Stacking and Blending models for urban waterlogging susceptibility assessment but also provides valuable insights for waterlogging mitigation strategies in urban planning. [ABSTRACT FROM AUTHOR]