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Forecasting road network functionality states during extreme rainfall events to facilitate real-time emergency response planning

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  • Wang, Junyan
  • Wang, Naiyu

Abstract

Rapid prediction of Road Network Functionality (RNF) during extreme rainfall-induced flooding is crucial for supporting proactive and real-time emergency planning, such as rescue, evacuation planning, and emergency supply distribution. Unlike normal operational conditions, extreme rainfall events introduce complex non-stationary, non-Euclidean characteristics to RNF due to intricate meteorological and hydrological processes, as well as the role of a community's road network in emergency response planning. Conventional physics-based flood simulations and flow-based road network analyses typically lack the computational efficiency required for real-time RNF predictions, hindering timely risk mitigation decisions. This study leverages the accuracy of physics-based simulations and the efficacy of deep-learning technologies to develop a deep learning-based surrogate model for Rain-to-RNF (R2R) predictions. This model couples Long Short-Term Memory (LSTM) networks with Spatial-Temporal Graph Convolutional Networks (ST-GCNs) to uniquely capture the spatiotemporal dynamics of RNF under extreme rainfall events. The predictive accuracy, stability, and versatility of the R2R surrogate model are demonstrated in four flood-prone communities in Zhejiang Province. Its implementation during Typhoon Fitow (2013) over a 30-hour intense rainfall showcases its promising predictive capacity and unparalleled computational efficiency. This research advances disaster management, enhancing the resilience and responsiveness of community infrastructure during extreme weather events.

Suggested Citation

  • Wang, Junyan & Wang, Naiyu, 2024. "Forecasting road network functionality states during extreme rainfall events to facilitate real-time emergency response planning," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
  • Handle: RePEc:eee:reensy:v:252:y:2024:i:c:s0951832024005246
    DOI: 10.1016/j.ress.2024.110452
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    1. Dongli Jia & Zhao Li & Yongle Dong & Xiaojun Wang & Mingcong Lin & Kaiyuan He & Xiaoyu Yang & Jiajing Liu, 2025. "Restoration Strategy for Urban Power Distribution Systems Considering Coupling with Transportation Networks Under Heavy Rainstorm Disasters," Energies, MDPI, vol. 18(2), pages 1-18, January.

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