Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, 77843, USA.
Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA.
Sci Rep. 2020 Aug 10;10(1):13481. doi: 10.1038/s41598-020-70524-x.
In this study, we propose a contagion model as a simple and powerful mathematical approach for predicting the spatial spread and temporal evolution of the onset and recession of floodwaters in urban road networks. A network of urban roads resilient to flooding events is essential for the provision of public services and for emergency response. The spread of floodwaters in urban networks is a complex spatial-temporal phenomenon. This study presents a mathematical contagion model to describe the spatial-temporal spread and recession process of floodwaters in urban road networks. The evolution of floods within networks can be captured based on three macroscopic characteristics-flood propagation rate ([Formula: see text]), flood incubation rate ([Formula: see text]), and recovery rate ([Formula: see text])-in a system of ordinary differential equations analogous to the Susceptible-Exposed-Infected-Recovered (SEIR) model. We integrated the flood contagion model with the network percolation process in which the probability of flooding of a road segment depends on the degree to which the nearby road segments are flooded. The application of the proposed model is verified using high-resolution historical data of road flooding in Harris County during Hurricane Harvey in 2017. The results show that the model can monitor and predict the fraction of flooded roads over time. Additionally, the proposed model can achieve 90% precision and recall for the spatial spread of the flooded roads at the majority of tested time intervals. The findings suggest that the proposed mathematical contagion model offers great potential to support emergency managers, public officials, citizens, first responders, and other decision-makers for flood forecast in road networks.
在这项研究中，我们提出了一个传染病模型，作为一种简单而强大的数学方法，用于预测城市道路网络中洪水的起始和消退的空间传播和时间演变。具有抗洪水能力的城市道路网络对于提供公共服务和应急响应至关重要。洪水在城市网络中的传播是一个复杂的时空现象。本研究提出了一个数学传染病模型来描述城市道路网络中洪水的时空传播和消退过程。可以基于三个宏观特征——洪水传播率 ([Formula: see text])、洪水潜伏期 ([Formula: see text]) 和恢复率 ([Formula: see text])——在类似于易感-暴露-感染-恢复 (SEIR) 模型的常微分方程组中捕获网络内洪水的演变。我们将洪水传染病模型与网络渗流过程相结合，其中道路段的洪水概率取决于附近道路段被洪水淹没的程度。该模型在 2017 年哈里斯县飓风哈维期间的高分辨率历史道路洪水数据中进行了验证。结果表明，该模型可以实时监测和预测被洪水淹没的道路比例。此外，该模型在大多数测试时间间隔内，对洪水淹没道路的空间传播可以达到 90%的精度和召回率。研究结果表明，所提出的数学传染病模型具有很大的潜力，可以为道路网络中的洪水预测为应急管理人员、公职人员、公民、第一响应者和其他决策者提供支持。
