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Zhengzhou subway flooding a warning for other major cities

https://doi.org/10.1126/science.abl6944
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inferred← citeshttps://doi.org/10.2139/ssrn.4809481openalex-extracted
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inferred← citeshttps://doi.org/10.1016/j.compenvurbsys.2024.102160openalex-snapshot
Predicting human mobility flows in response to extreme urban floods: A hybrid deep learning model considering spatial heterogeneity
inferred← citeshttps://doi.org/10.1016/j.ijdrr.2022.102800openalex-snapshot
Surprise is inevitable: How do we train and prepare to make our critical infrastructure more resilient?
inferred← citeshttps://doi.org/10.1016/j.tust.2023.105271openalex-snapshot
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inferred← citeshttps://doi.org/10.1029/2021wr031279openalex-snapshot
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inferred← citeshttps://doi.org/10.1061/jmenea.meeng-6503openalex-snapshot
Public Responses to Operator Information Disclosure in Urban Infrastructure Accidents
inferred← citeshttps://doi.org/10.1093/nsr/nwad097openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/itsc58415.2024.10920262openalex-snapshot
Resilience Assessment and Optimization of Urban Rail Systems with Train Platoons: A Passenger Demand Oriented Approach
inferred← citeshttps://doi.org/10.1109/tits.2023.3236004openalex-snapshot
Resilience-Oriented Train Rescheduling Optimization in Railway Networks: A Mixed Integer Programming Approach
inferred← citeshttps://doi.org/10.1126/sciadv.abo1638openalex-snapshot
Global concurrent climate extremes exacerbated by anthropogenic climate change
inferred← citeshttps://doi.org/10.2139/ssrn.4882884openalex-snapshot
Understanding the Key Influencing Factors of Urban Resilience in Zhengzhou City, China: Quantification Using Spatial Machine Learning and the Density-Structure-Function Framework
inferred← citeshttps://doi.org/10.3390/w15081528openalex-snapshot
State Selection and Cost Estimation for Deep Reinforcement Learning-Based Real-Time Control of Urban Drainage System
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