Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

Abstract

This paper presents a Bayesian network model to assess the vulnerability of the flood control infrastructure and to simulate failure cascade based on the topological structure of flood control networks along with hydrological information gathered from sensors. Two measures are proposed to characterize the flood control network vulnerability and failure cascade: (a) node failure probability (NFP), which determines the failure likelihood of each network component under each scenario of rainfall event, and (b) failure cascade susceptibility, which captures the susceptibility of a network component to failure due to failure of other links. The proposed model was tested in both single watershed and multiple watershed scenarios in Harris County, Texas using historical data from three different flooding events, including Hurricane Harvey in 2017. The proposed model was able to identify the most vulnerable flood control network segments prone to flooding in the face of extreme rainfall. The framework and results furnish a new tool and insights to help decision-makers to prioritize infrastructure enhancement investments and actions. The proposed Bayesian network modeling framework also enables simulation of failure cascades in flood control infrastructures, and thus could be used for scenario planning as well as near-real-time inundation forecasting to inform emergency response planning and operation, and hence improve the flood resilience of urban areas. 668 wileyonlinelibrary.com/journal/mice Comput Aided Civ Inf. 2020;35:668-684.

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

“…The current flooding warning systems such as Harris County Flood Warning System (HCFWS, 2019) provide monitoring (based on flood stream gauges). Additionally, there are methods such as Bayesian network model that enable vulnerability assessment in urban areas (Dong, Yu, Farahmand, & Mostafavi, 2019). However, these systems and models provide limited predictive flood warning capabilities.…”

“…The ability to use H&H for predictive flood warning and situation awareness as a flooding event unfolds is rather limited. Because it is critical to acquire the flood information in a predictive fashion for emergency response operations, an accurate network failure (i.e., overflow) prediction model is needed to capture the spatial and temporal failure cascading process to better protect citizens and infrastructures from the flooding (Dong, Yu et al., 2019). Various methods have been proposed to support real‐time flood forecast, including the U.S. National Weather service ensemble forecasting (Koren et al., 1999), fuzzy reasoning method (Liong, Lim, Kojiri, & Hori, 2000), the river flow forecast system (Moore, Jones, Bird, & Cottingham, 1990), neural networks (Thirumalaiah & Deo, 1998; Besaw, Rizzo, Bierman, & Hackett, 2010), transfer function methods (Young, 2002), functional networks (Bruen & Yang, 2005), and generalized likelihood uncertainty estimation (GLUE) (Romanowicz & Beven, 2003).…”

Real‐time regional seismic damage assessment framework based on long short‐term memory neural network

“…More advanced deep learning methods can also be explored. For example, Bayesian networks can be used for evaluating the estimation uncertainty (Dong, Yu, Farahmand, & Mostafavi, 2020; Wang, Liu, & Ni, 2018). In addition, the dynamic interactions among the buildings and sites are not considered in this study.…”

Vulnerability Assessment of the Urban Rail Transit Network Based on Travel Behavior Analysis

“…Moreover, a series of models including the capacity-load model [17][18][19], binary influence model [20,21], sandpile model [22], ORNL-PSerc-Alaska (OPA) [23], CASCADE model [24], and coupled map lattice model [25], has been proposed to describe the cascading failure phenomenon of the URTN. The current research mainly focuses on the capacity-load model, which assumes that the capacity is proportional to the initial load and is mainly applied to simulate the cascading failure of the network [26].…”