Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Farahmand, Hamed"'
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
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 potential of crowdsourced data for flood mapping, little attention has b
Externí odkaz:
http://arxiv.org/abs/2207.05797
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
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
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 features. We present a new computational modeling framework including an a
Externí odkaz:
http://arxiv.org/abs/2111.08450
Autor:
Farahmand, Hamed, Yin, Kai, Hsu, Chia-Wei, Savadogo, Ibrahim, Espinet Alegre, Xavier, Mostafavi, Ali
Publikováno v:
In Transportation Research Part D August 2024 133
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 study is to create and test a hybrid deep learning model, FastGRNN-FCN (Fast, Accurate, Stable and Tiny Gated Recurrent Neural Network-Fully Convolutional Network), for urban flood prediction and situation awareness using channe
Externí odkaz:
http://arxiv.org/abs/2006.09201
Publikováno v:
In Transportation Research Part D December 2023 125