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.
A digital map of the built environment is useful for a range of economic, emergency response, and urban planning exercises such as helping find places in app driven interfaces, helping emergency managers know what locations might be impacted by a flood or fire, and helping city planners proactively identify vulnerabilities and plan for how a city is growing. Since its inception in 2004, OpenStreetMap (OSM) sets the benchmark for open geospatial data and has become a key player in the public, research, and corporate realms. Following the foundations laid by OSM, several open geospatial products describing the built environment have blossomed including the Microsoft USA building footprint layer and the OpenAddress project. Each of these products use different data collection methods ranging from public contributions to artificial intelligence, and if taken together, could provide a comprehensive description of the built environment. Yet, these projects are still siloed, and their variety makes integration and interoperability a major challenge. Here, we document an approach for merging data from these three major open building datasets and outline a workflow that is scalable to the continental United States (CONUS). We show how the results can be structured as a knowledge graph over which machine learning models are built. These models can help propagate and complete unknown quantities that can then be leveraged in disaster management.
more » « less- NSF-PAR ID:
- 10366763
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AI Magazine
- Volume:
- 43
- Issue:
- 1
- ISSN:
- 0738-4602
- Page Range / eLocation ID:
- p. 40-45
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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