Social Sensing in Disaster City Digital Twin: Integrated Textual–Visual–Geo Framework for Situational Awareness during Built Environment Disruptions

Fan, Chao; Jiang, Yucheng; Mostafavi, Ali

doi:10.1061/(asce)me.1943-5479.0000745

Cited by 107 publications

(55 citation statements)

References 49 publications

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“…The use of sensors for registering flood‐related data such as rainfall level and water level in flood gauges as well as the advancements in gathering near real‐time crowdsourced data reporting flood events creates excellent opportunities for developing ML models for flood prediction (Fan, Jiang, & Mostafavi, 2020). Moreover, researchers have developed more computationally efficient ML techniques for multivariate time series prediction that enable highly accurate forecasts when sufficient data entry is available.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Dong

Farahmand

et al. 2020

Computer aided Civil Eng

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The objective of this study is to create and test a hybrid deep learning (DL) model, FastGRNN‐FCN (fast, accurate, stable and tiny gated recurrent neural network‐fully convolutional network), for urban flood prediction and situation awareness using channel network sensors data. The study used Harris County, Texas, as the testbed, and obtained channel sensor data from three historical flood events (e.g., 2016 Tax Day Flood, 2016 Memorial Day Flood, and 2017 Hurricane Harvey Flood) for training and validating the hybrid DL model. The flood data are divided into a multivariate time series and used as the model input. Each input comprises nine variables, including information of the studied channel sensor and its predecessor and successor sensors in the channel network. Precision‐recall curve and F‐measure are used to identify the optimal set of model parameters. The optimal model with a weight of 1 and a critical threshold of 0.59 are obtained through 100 iterations based on examining different weights and thresholds. The test accuracy and F‐measure eventually reach 97.8% and 0.792, respectively. The model is then tested in predicting the 2019 Imelda Flood in Houston and the results show an excellent match with the empirical flood. The results show that the model enables accurate prediction of the spatial–temporal flood propagation and recession and provides emergency response officials with a predictive flood warning tool for prioritizing the flood response and resource allocation strategies.

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Section: Literature Reviewmentioning

confidence: 99%

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

Dong

Farahmand

et al. 2020

Computer aided Civil Eng

Self Cite

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“…For that purpose, the city digital twin is perceived as enabling technology to promote situational awareness for city management and to provide a city information model; that is, the city digital twin can collect, monitor, and manage city data [34]. For instance, it can reflect citizens' health condition [58]; represent, reason, and analyze energy consumption data [33,55,65]; detect motion for public security activities [42]; monitor noise pollution in the city using dynamics modeling [61]; provide real-time tracking of information during disasters and localize vulnerable objects [50,56]; and track and monitor individuals' behavior and localize disruptions and potential risks for emergency and disaster management [57,64,68]. It is also anticipated to enhance risk analysis and prevention and identify information flows for disaster management [60,69].…”

Section: Situational Awarenessmentioning

confidence: 99%

“…Furthermore, the size and complexity of the city data shed a light on the necessity of developing widely accepted standards for the data models and design schemas [27,37] to facilitate the development of the city models, in addition to gaining its benefits in reducing time, cost, and errors. Furthermore, data accessibility can be challenging due to ownership and expensiveness [44,68].…”

Section: Challenges To the Full Utilization Of City Digital Twin Potementioning

confidence: 99%

City Digital Twin Potentials: A Review and Research Agenda

2021

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The city digital twin is anticipated to accurately reflect and affect the city’s functions and processes to enhance its realization, operability, and management. Although research on the city digital twin is still in its infancy, the advancement of the digital twin technology is growing fast and providing viable contributions to augmenting smart city developments. This study reviews the literature to identify the current and prospective potentials and challenges of digital twin cities. A research agenda is also proposed to guide future research on the city digital twincity digital twin to reach the utmost level of a comprehensive and complete city digital twin. Enhancing the efficiency of data processing, promoting the inclusion of socio-economic components of the city, and developing mutual integration between the two counterparts of the digital twin are proposed to be the future research directions to achieve and utilize a completely mirrored city digital twin.

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“…Second, the majority of existing studies related to analyzing the geographical information on social media primarily employ the geotags from the posts to specify location insights. One branch of studies has focused on analyzing the geotagging behaviors of people in disasters [23], [24]. For example, Kumar et al proposed an approach to identify whether a tweet is generated from crisis regions based on the historical geotags [25].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Machine Learning Pipeline for Automated Mapping of Events and Locations From Social Media in Disasters

Fan

Mostafavi

2020

IEEE Access

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The objective of this study is to propose and test a hybrid machine learning pipeline to uncover the unfolding of disaster events corresponding to different locations from social media posts during disasters. Effective disaster response and recovery require a comprehensive understanding of disaster situations, i.e., unfolding of disaster events and geographic distribution of the disruptions. Existing studies have employed machine learning methods to conduct coarse-grained event detection and analyze the geographical location information from geotagged social media data. However, only a very small fraction of the entire set of social media data includes geotagged information, which may not directly correspond to events described in the content of posts. In addition, the coarse-grained information detected by existing approaches is tokenbased, which does not provide sufficient information for situation awareness. Hence, the detection of location and finer-grained event information could significantly improve the utility, credibility, and interpretability of social media data for situation awareness. To address these limitations, this study proposed a hybrid machine learning pipeline that makes use of all relevant tweets to uncover the evolution of disaster events across different locations. The pipeline integrates Named Entity Recognition for detecting locations mentioned in the posts, location fusion approach to extract coordinates of the locations and remove noise information, fine-tuned BERT model for classifying posts with humanitarian categories, and graph-based clustering to identify credible situational information. The application of the study is demonstrated using the data set collected from Twitter during the 2017 Hurricane Harvey in Houston. The results show the capability of the proposed hybrid pipeline for automated mapping of events across time and space from social media posts with considerable accuracy. The findings also suggest that the potential for forensic analysis of disasters using mapped events and their evolution, and based on the variation of social media attention to different locations in disasters. Hence, this method could provide a useful tool to support emergency managers, public officials, residents, first responders, and other stakeholders in rapid situation awareness across time and space. INDEX TERMS Machine learning pipeline, social media, disasters, automated mapping.

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Social Sensing in Disaster City Digital Twin: Integrated Textual–Visual–Geo Framework for Situational Awareness during Built Environment Disruptions

Cited by 107 publications

References 49 publications

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

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

City Digital Twin Potentials: A Review and Research Agenda

A Hybrid Machine Learning Pipeline for Automated Mapping of Events and Locations From Social Media in Disasters

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