Search

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R 2 of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model's interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively. [ABSTRACT FROM AUTHOR]

In studying resilience in temporal human networks, relying solely on global network measures would be inadequate; latent sub-structural network mechanisms need to be examined to determine the extent of impact and recovery of these networks during perturbations, such as urban flooding. In this study, we utilize high-resolution aggregated location-based data to construct temporal human mobility networks in Houston in the context of the 2017 Hurricane Harvey. We examine motif distribution, motif persistence, temporal stability, and motif attributes to reveal latent sub-structural mechanisms related to the resilience of human mobility networks during disaster-induced perturbations. The results show that urban flood impacts persist in human mobility networks at the sub-structure level for several weeks. The impact extent and recovery duration are heterogeneous across different network types. Also, while perturbation impacts persist at the sub-structure level, global topological network properties indicate that the network has recovered. The findings highlight the importance of examining the microstructures and their dynamic processes and attributes in understanding the resilience of temporal human mobility networks (and other temporal networks). The findings can also provide disaster managers, public officials, and transportation planners with insights to better evaluate impacts and monitor recovery in affected communities. [ABSTRACT FROM AUTHOR]

Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial–temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold: first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features (e.g., rainfall intensity and water elevation) and human-sensed data (e.g., residents' flood reports and fluctuations of human activity) on flood nowcasting. Third, its attention mechanism enables the model to direct its focus to the most influential features that vary dynamically and influence the flood nowcasting. We show the application of the modeling framework in the context of Harris County, Texas, as the study area and 2017 Hurricane Harvey as the flood event. Three categories of features are used for nowcasting the extent of flood inundation in different census tracts: (i) static features that capture spatial characteristics of various locations and influence their flood status similarity, (ii) physics-based dynamic features that capture changes in hydrodynamic variables, and (iii) heterogeneous human-sensed dynamic features that capture various aspects of residents' activities that can provide information regarding flood status. Results indicate that the ASTGCN model provides superior performance for nowcasting of urban flood inundation at the census-tract level, with precision 0.808 and recall 0.891, which shows the model performs better compared with other state-of-the-art models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting. Given the results of the comparisons of the models, the proposed modeling framework has the potential to be more investigated when more data of historical events are available in order to develop a predictive tool to provide community responders with an enhanced prediction of the flood inundation during urban flood. [ABSTRACT FROM AUTHOR]

The current characterization of flood exposure is largely based on residential location of populations; however, location of residence only partially captures the extent to which populations are exposed to flood hazards. An important, yet under-recognized aspect of flood exposure is associated with human mobility patterns and population visitation to places located in flood prone areas. In this study, we analyzed large-scale, high-resolution location-intelligence data collected from anonymous mobile phone users to characterize human mobility patterns and the resulting flood exposure in coastal counties of the United States. We developed the metric of mobility-based exposure based on dwell time in places located in the 100-year floodplain. The results of examining the extent of mobility-based flood exposure across demographic groups reveal significant disparities across race, income, and education level groups. The results show that Black and Asian, economically disadvantaged, and undereducated populations in US coastal cities are disproportionally exposed to flood due to their daily mobility activities, indicating a pattern contrary to that of residential flood exposure. The results suggest that mobility behaviors play an important role in extending flood exposure reach disproportionally among different socio-demographic groups. The results highlight that urban flood risk assessments should not only focus on the level of flood exposure to residences, but also should consider mobility-based exposure to better learn the disparities in flood exposure among social groups. Mobility-based flood exposure provides a new perspective regarding the extent to which floods could disrupt people's life activities and enable a better characterization of disparity in populations' exposure to flood hazards beyond their place of residence. The findings of this study have important implications for urban planners, flood managers, and city officials in terms of accounting for mobility-based flood exposure in flood risk management plans and actions. [ABSTRACT FROM AUTHOR]

Urban flooding disrupts traffic networks, affecting mobility and disrupting residents' access. Flooding events are predicted to increase due to climate change; therefore, understanding traffic network's flood-caused disruption is critical to improving emergency planning and city resilience. This study reveals the anatomy of perturbed traffic networks by leveraging high-resolution traffic network data from a major flood event and advanced high-order network analysis. We evaluate travel times between every pairwise junction in the city and assess higher-order network geometry changes in the network to determine flood impacts. The findings show network-wide persistent increased travel times could last for weeks after the flood water has receded, even after modest flood failure. A modest flooding of 1.3% road segments caused 8% temporal expansion of the entire traffic network. The results also show that distant trips would experience a greater percentage increase in travel time. Also, the extent of the increase in travel time does not decay with distance from inundated areas, suggesting that the spatial reach of flood impacts extends beyond flooded areas. The findings of this study provide an important novel understanding of floods' impacts on the functioning of traffic networks in terms of travel time and traffic network geometry. • High-order network analysis and reveals anatomy of perturbed traffic networks. • Network-wide persistent travel time increase can last for weeks. • Modest flooding causes significant temporal expansion of network. • Distant trips experience greater percentage increase in travel time. • Flood impacts extend beyond inundated areas. [ABSTRACT FROM AUTHOR]

Pluvial flash floods are fast-moving hazards and causes significant disruptions in urban areas. With the increase in heavy precipitations, the ability to proactively identify flash floods hotspots in cities is critical for flood nowcasting and predictive monitoring of risks. While rainfall runoff models and hydrologic models are useful models for flash flood prediction, these models are computationally expensive and effort intensive to be used for flood nowcasting. To address this challenge, this study presents interpretable machine learning models for predicting urban flash flood hotspots based on intertwined land and built environment features. The task of predicting flash flood hotspots is formulated as a binary classification problem, and three recent flash flood events in U.S. cities are selected for data collection and model validation. Various features related to land and built environment characteristics are constructed using diverse datasets, and the occurrences of flash floods are captured using crowdsource data from the events. Using these features and datasets, the flash flood hotspots of cities are predicted with two ensemble models based on decision trees. The results demonstrate that the models can achieve good accuracy (0.8) in identifying flooded/non-flooded locations. Especially, the models can achieve high true positive rate (0.83–0.89) and low missing rate, demonstrating the methods' practicability for accurately predicting flooded hotspots. The model interpretation results indicate that land features related to hydrological and topological features have greater impacts on flash flood risk, than built environment features. Further analysis reveals that the feature importance, model performance, and model transferability performance vary among cities and localized specifications of the models are needed for accurate prediction of flash flood for a particular city. The data-driven machine learning models presented in this study provide a useful tool for predicting flash flood hotspots based on the intertwined features of land and the built environment in cities to enable nowcasting and proactive monitoring of flash flood hotspots for emergency response and also inform integrated urban design and development towards flash flood risk reduction. • Interpretable machine learning models are utilized to provide reliable prediction of flash flood hotspots. • Built environment variables are extracted for constructing model features. • The models can achieve very good accuracy in identifying flooded/non-flooded locations. • Hydrological and topological features have greater impacts on flash flood risk than other features • Localized specifications of models are needed for accurate flash flood prediction for particular cities [ABSTRACT FROM AUTHOR]

This study uses mobility data in the context of 2017 Hurricane Harvey in Harris County to examine the impact of flooding on access to dialysis centers. We examined access dimensions using static and dynamic metrics. The static metric is the shortest distance from census block groups to the closest centers. Dynamic metrics are: 1) redundancy (daily unique number of centers visited), 2) frequency (daily number of visits to dialysis centers), and 3) proximity (visits weighted by distance to dialysis centers). The results show that: the extent of dependence of regions on dialysis centers varies; flooding significantly reduces access redundancy and frequency of dialysis centers; regions with a greater minority percentage and lower household income were likely to experience extensive disruptions; high-income regions more quickly revert to pre-disaster levels; larger centers located in non-flooded areas are critical to absorbing the unmet demand from disrupted facilities. [ABSTRACT FROM AUTHOR]

The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments' current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that model 1 and model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2–4 h) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans • Using high-resolution traffic data to predict near-future road inundation status in urban floods • Adjusting spatio-temporal graph convolutional networks for road network inundation status predictions • Contributing near-future infrastructure failure prediction capability to the smart flood resilience [ABSTRACT FROM AUTHOR]

• Observability considerations is neglected in the design of flood sensor networks. • A framework for the assessment of flood control network observability is proposed. • The framework enables monitoring flood status across critical regions. • The framework is tested on flood control network in Harris County, TX. • Results reveal insights from observability consideration in sensor network design. Monitoring the state of infrastructure systems proactively is crucial to ensure their proper functionality during extreme events. Flood control networks are designed to keep communities safe from inundation. Accurately monitoring the inundation status of flood control components could enhance flood situational awareness and risk management during extreme events. However, the design and placement of sensor networks that collect data to monitor the status of flooding do not often consider the principles of observability. We bridge the gap by creating a framework for the assessment of flood control network observability and determining the minimum number and locations of the flood gauges to achieve maximum monitoring across critical regions. We first delineate critical regions that are needed to be observed, then identify feasible solutions on sensor sets, and finally determine the candidate sensor set based on the importance of the nodes that exist in each sensor set. We tested the framework in the context of Harris County, Texas, as a case study. The results show that the current flood gauge placement is not sufficient for comprehensively monitoring the flooding status of critical areas, nor informed by the network observability principles. Results also offer insights to decision-makers to extend the current flood gauge network or design new flood gauge networks more effectively. [ABSTRACT FROM AUTHOR]