Smart flood resilience: harnessing community-scale big data for predictive flood risk monitoring, rapid impact assessment, and situational awareness

• Published in: Environmental research, infrastructure and sustainability : ERIS Vol. 2; no. 2; pp. 25006 - 25028
• Main Authors: Yuan, Faxi, Fan, Chao, Farahmand, Hamed, Coleman, Natalie, Esmalian, Amir, Lee, Cheng-Chun, Patrascu, Flavia I, Zhang, Cheng, Dong, Shangjia, Mostafavi, Ali
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.06.2022
• Subjects: big data; machine learning; smart resilience; urban AI
• ISSN: 2634-4505; 2634-4505
• DOI: 10.1088/2634-4505/ac7251

Abstract Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk monitoring and situational awareness. The smart flood resilience framework focuses on four core capabilities that could be augmented by the use of heterogeneous community-scale big data and analytics techniques: (1) predictive flood risk mapping; (2) automated rapid impact assessment; (3) predictive infrastructure failure prediction and monitoring; and (4) smart situational awareness capabilities. We demonstrate the components of these core capabilities of the smart flood resilience framework in the context of the 2017 Hurricane Harvey in Harris County, Texas. First, we present the use of flood sensors for the prediction of floodwater overflow in channel networks and inundation of co-located road networks. Second, we discuss the use of social media and machine learning techniques for assessing the impacts of floods on communities and sensing emotion signals to examine societal impacts. Third, we describe the use of high-resolution traffic data in network-theoretic models for nowcasting of flood propagation on road networks and the disrupted access to critical facilities, such as hospitals. Fourth, we introduce how location-based and credit card transaction data were used in spatial analyses to proactively evaluate the recovery of communities and the impacts of floods on businesses. These analyses show that the significance of core capabilities of the smart flood resilience framework in helping emergency managers, city planners, public officials, responders, and volunteers to better cope with the impacts of catastrophic flooding events.