Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Yuan, Faxi"'
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-based approaches to examining community recovery, however, have signif
Externí odkaz:
http://arxiv.org/abs/2211.11100
Unveiling Vulnerability and Inequality in Disrupted Access to Dialysis Centers During Urban Flooding
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 data in the context of the 2017 Hurricane Harvey in Harris County to e
Externí odkaz:
http://arxiv.org/abs/2208.09425
Autor:
Lee, Cheng-Chun, Rajput, Akhil, Hsu, Chia-Wei, Fan, Chao, Yuan, Faxi, Dong, Shangjia, Esmalian, Amir, Farahmand, Hamed, Patrascu, Flavia Ioana, Liu, Chia-Fu, Li, Bo, Ma, Junwei, Mostafavi, Ali
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 help transportation agencies incorporate resilience considerations into
Externí odkaz:
http://arxiv.org/abs/2205.02758
Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not eq
Externí odkaz:
http://arxiv.org/abs/2201.00745
Autor:
Yuan, Faxi, Fan, Chao, Farahmand, Hamed, Coleman, Natalie, Esmalian, Amir, Lee, Cheng-Chun, Patrascu, Flavia I., Zhang, Cheng, Dong, Shangjia, Mostafavi, Ali
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 heterogeneou
Externí odkaz:
http://arxiv.org/abs/2111.06461
Autor:
Yuan, Faxi, Mobley, William, Farahmand, Hamed, Xu, Yuanchang, Blessing, Russell, Dong, Shangjia, Mostafavi, Ali, Brody, Samuel D.
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 monitoring of road network flooding status plays an essential role in
Externí odkaz:
http://arxiv.org/abs/2108.13265
The objective of this research is to explore the temporal importance of community-scale human activity features for rapid assessment of flood impacts. Ultimate flood impact data, such as flood inundation maps and insurance claims, becomes available o
Externí odkaz:
http://arxiv.org/abs/2106.08370
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 monitori
Externí odkaz:
http://arxiv.org/abs/2104.02276
The objective of this study is to examine spatial patterns of impacts and recovery of communities based on variances in credit card transactions. Such variances could capture the collective effects of household impacts, disrupted accesses, and busine
Externí odkaz:
http://arxiv.org/abs/2101.10090
This study aims to quantify community resilience based on fluctuations in the visits to various Point-of-Interest (POIs) locations. Visit to POIs is an essential indicator of human activities and captures the combined effects of perturbations in peop
Externí odkaz:
http://arxiv.org/abs/2011.07440