Abstract：
A rapid and comprehensive assessment of flood impacts is critical to assist emergency managers in conducting effective relief operations. With advances in information technologies, various types of sensors have been widely used to assess flood impacts promptly as they are capable of providing rapid flood impact information. However, sensor-driven approaches are limited in the provision of a comprehensive impact assessment as sensors are often sparsely distributed. In this research, the authors integrate the sparse flood impact information obtained from sensors and the spatial autocorrelation of flood-impacted areas, in order to achieve a rapid and comprehensive flood impact assessment. To achieve such a purpose, a systematic approach is proposed to (1) extract flood impact information from sparsely distributed sensors; (2) model the spatial autocorrelation of flood-impacted areas based on flood evolution and geography principles; (3) learn the parameters of the spatial autocorrelation model through a gradient descent method; (4) infer the flood impacts of sensor-uncovered areas based on the sparsely sensed impacts and the modeled spatial autocorrelation. To illustrate the proposed approach, we studied flood impacts on Highways in Houston, Texas during Hurricane Harvey. Results show that the spatial autocorrelation model presents a decent generalization capability in inferring the probability of neighboring highway blocks having the same flood impacts. Compared to purely sensor-driven approaches, the proposed approach is capable of greatly extending the coverage of flood impact assessment while maintaining the nearly same accuracy.
摘要：
快速全面评估洪水影响对于协助应急管理人员开展有效的救援行动至关重要。随着信息技术的进步，各种类型的传感器已被广泛用于及时评估洪水影响，因为它们能够提供快速的洪水影响信息。然而,传感器驱动的方法在提供全面的影响评估方面受到限制，因为传感器通常分布稀疏。在这项研究中,作者集成了从传感器获得的稀疏洪水影响信息和洪水影响区域的空间自相关，以实现快速全面的洪水影响评估。为了达到这样的目的,提出了一种系统的方法：（1）从稀疏分布的传感器中提取洪水影响信息；（2）基于洪水演变和地理原理对洪水影响区域的空间自相关建模；（3）通过梯度下降法学习空间自相关模型的参数；（4）根据稀疏感知的影响和建模的空间自相关推断传感器未覆盖区域的洪水影响。为了说明拟议的方法，我们研究了洪水对休斯顿高速公路的影响，飓风哈维期间的德克萨斯州。结果表明，空间自相关模型在推断相邻公路街区具有相同洪水影响的概率方面具有良好的泛化能力。与纯传感器驱动的方法相比，所提出的方法能够大大扩展洪水影响评估的覆盖范围，同时保持几乎相同的准确性。
