8 citations · 23 across the 8 of their papers we have counts for
8 papers
Data-driven Tracking of the Bounce-back Path after Disasters: Critical Milestones of Population Activity Recovery and Their Spatial Inequality
Yuqin Jiang, Faxi Yuan, Hamed Farahmand +3
The ability to measure and track the speed and trajectory of a community's post-disaster recovery is essential to inform resource allocation and prioritization. The current survey-…
Unveiling Vulnerability and Inequality in Disrupted Access to Dialysis Centers During Urban Flooding
Faxi Yuan, Hamed Farahmand, Russell Blessing +2
Despite the criticality of dialysis facilities, limited knowledge exists regarding the extent and inequality of disrupted access caused by weather events. This study uses mobility…
Examining Data Imbalance in Crowdsourced Reports for Improving Flash Flood Situational Awareness
Miguel Esparza, Hamed Farahmand, Samuel Brody +1
The use of crowdsourced data has been finding practical use for enhancing situational awareness during disasters. While recent studies have shown promising results regarding the po…
Quantitative Measures for Integrating Resilience into Transportation Planning Practice: Study in Texas
Cheng-Chun Lee, Akhil Rajput, Chia-Wei Hsu +10
The objective of this study is to propose a system-level framework with quantitative measures to assess the resilience of road networks. The framework proposed in this paper can he…
Smart Flood Resilience: Harnessing Community-Scale Big Data for Predictive Flood Risk Monitoring, Rapid Impact Assessment, and Situational Awareness
Faxi Yuan, Chao Fan, Hamed Farahmand +7
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 demo…
A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features
Hamed Farahmand, Yuanchang Xu, Ali Mostafavi
The objective of this study is to develop and test a novel structured deep-learning modeling framework for urban flood nowcasting by integrating physics-based and human-sensed feat…
Predicting Road Flooding Risk with Machine Learning Approaches Using Crowdsourced Reports and Fine-grained Traffic Data
Faxi Yuan, William Mobley, Hamed Farahmand +5
The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Predictive flood…
A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data
Shangjia Dong, Tianbo Yu, Hamed Farahmand +1
The objective of this study is to create and test a hybrid deep learning model, FastGRNN-FCN (Fast, Accurate, Stable and Tiny Gated Recurrent Neural Network-Fully Convolutional Net…