Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Shangjia Dong"'
Autor:
Faxi Yuan, Cheng-Chun Lee, William Mobley, Hamed Farahmand, Yuanchang Xu, Russell Blessing, Shangjia Dong, Ali Mostafavi, Samuel D. Brody
Publikováno v:
Computational Urban Science, Vol 3, Iss 1, Pp 1-16 (2023)
Abstract The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Existing road inundation studies either lack empirical data for model validati
Externí odkaz:
https://doaj.org/article/9fe750a8adfa429197bf4a1a973f2fe5
Publikováno v:
Communications Earth & Environment, Vol 3, Iss 1, Pp 1-10 (2022)
Flood impacts on the functioning of a transport network are substantially exacerbated by indirect effects such as congestion due to changing traffic patterns, suggests an analysis of the impact of Hurricane Harvey on transport in Harris County, Texas
Externí odkaz:
https://doaj.org/article/b5b4c8ee9f82458f8dc08560ef42ec0d
Autor:
Faxi Yuan, Chao Fan, Hamed Farahmand, Natalie Coleman, Amir Esmalian, Cheng-Chun Lee, Flavia I Patrascu, Cheng Zhang, Shangjia Dong, Ali Mostafavi
Publikováno v:
Environmental Research: Infrastructure and Sustainability, Vol 2, Iss 2, p 025006 (2022)
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:
https://doaj.org/article/82334e208e6b46b397087ddd980ef02b
Publikováno v:
PLoS ONE, Vol 14, Iss 11, p e0224522 (2019)
This paper proposes and tests a multilayer framework for simulating the network dynamics of inter-organizational coordination among interdependent infrastructure systems (IISs) in resilience planning. Inter-organizational coordination among IISs (suc
Externí odkaz:
https://doaj.org/article/1a44661e3d9d4250b3c452860d0d7e49
Publikováno v:
Environment and Planning B: Urban Analytics and City Science. 49:1838-1856
This paper presents a deep learning model based on the integration of physical and social sensors data for predictive watershed flood monitoring. The data from flood sensors and 3-1-1 reports data are mapped and fused through a multivariate time seri
Publikováno v:
Lifelines 2022.
Publikováno v:
Lifelines 2022.
Publikováno v:
Computing in Civil Engineering 2021.
Publikováno v:
Risk Analysis. 41:2336-2355
The objective of this article is to systematically assess and identify factors affecting risk disparity due to infrastructure service disruptions in extreme weather events. We propose a household service gap model that characterizes societal risks at
Publikováno v:
Reliability Engineering & System Safety. 232:109071