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Data on predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach
Interdependent infrastructure systems are vulnerable to the cascading effect of failures resulting from random failures and natural disasters. The data provided in this work is the processed data used for the proposed resilience assessment framework for interdependent water and transportation networ...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570939/ https://www.ncbi.nlm.nih.gov/pubmed/34765704 http://dx.doi.org/10.1016/j.dib.2021.107512 |
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author | Aslani, Babak Mohebbi, Shima |
author_facet | Aslani, Babak Mohebbi, Shima |
author_sort | Aslani, Babak |
collection | PubMed |
description | Interdependent infrastructure systems are vulnerable to the cascading effect of failures resulting from random failures and natural disasters. The data provided in this work is the processed data used for the proposed resilience assessment framework for interdependent water and transportation networks dealing with both types of failure [1]. The case study is the interconnected networks of water and transportation in Tampa, Florida. The data for the random failure is obtained from the developed algorithmic framework and the land use and social vulnerability data provided by the U.S. Census datasets. We then used a subset of this produced data to construct predictive models for the network resilience to random failures. As for the natural disaster scenario, we focused on hurricane Irma in 2017 as it directly affected the focused region in Florida. We used the specific guidelines and the raw flooding data for this hurricane, provided by FEMA, to estimate the standing water for each geographical area (polygons) and the associated network components. We labeled the areas as failed and undamaged based on the estimated water levels. Finally, we used this data for developing a geospatial Geographical Weighted Regression (GWR) model to predict the resilience in each polygon. We present the final dataset for water and transportation networks to facilitate reusability for any future resilience study in the selected urban area. |
format | Online Article Text |
id | pubmed-8570939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-85709392021-11-10 Data on predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach Aslani, Babak Mohebbi, Shima Data Brief Data Article Interdependent infrastructure systems are vulnerable to the cascading effect of failures resulting from random failures and natural disasters. The data provided in this work is the processed data used for the proposed resilience assessment framework for interdependent water and transportation networks dealing with both types of failure [1]. The case study is the interconnected networks of water and transportation in Tampa, Florida. The data for the random failure is obtained from the developed algorithmic framework and the land use and social vulnerability data provided by the U.S. Census datasets. We then used a subset of this produced data to construct predictive models for the network resilience to random failures. As for the natural disaster scenario, we focused on hurricane Irma in 2017 as it directly affected the focused region in Florida. We used the specific guidelines and the raw flooding data for this hurricane, provided by FEMA, to estimate the standing water for each geographical area (polygons) and the associated network components. We labeled the areas as failed and undamaged based on the estimated water levels. Finally, we used this data for developing a geospatial Geographical Weighted Regression (GWR) model to predict the resilience in each polygon. We present the final dataset for water and transportation networks to facilitate reusability for any future resilience study in the selected urban area. Elsevier 2021-10-27 /pmc/articles/PMC8570939/ /pubmed/34765704 http://dx.doi.org/10.1016/j.dib.2021.107512 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Aslani, Babak Mohebbi, Shima Data on predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach |
title | Data on predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach |
title_full | Data on predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach |
title_fullStr | Data on predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach |
title_full_unstemmed | Data on predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach |
title_short | Data on predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach |
title_sort | data on predictive resilience of interdependent water and transportation infrastructures: a sociotechnical approach |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570939/ https://www.ncbi.nlm.nih.gov/pubmed/34765704 http://dx.doi.org/10.1016/j.dib.2021.107512 |
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