Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach

<p>This study investigated the probability of road closure due to flooding. Logistic regression model was developed using the road closure data and the daily rainfall data for Houston, TX, USA during 2017 and 2018. The road network was further divided into flood prone zones. The spatial analys...

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Main Authors: H. Zhong, D. Liang
Format: Article
Language:English
Published: Copernicus Publications 2024-11-01
Series:Proceedings of the International Association of Hydrological Sciences
Online Access:https://piahs.copernicus.org/articles/386/345/2024/piahs-386-345-2024.pdf
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author H. Zhong
D. Liang
D. Liang
author_facet H. Zhong
D. Liang
D. Liang
author_sort H. Zhong
collection DOAJ
description <p>This study investigated the probability of road closure due to flooding. Logistic regression model was developed using the road closure data and the daily rainfall data for Houston, TX, USA during 2017 and 2018. The road network was further divided into flood prone zones. The spatial analysis revealed that the rainfall at the road segment level could be sufficiently represented by that recorded by nearest sensors. Within a 4 d window, the rainfall in the current day and 3 d prior played a more influential role in predicting road closure. The differential outcomes due to distinct regional features were explained. Finally, a watershed delineation approach substantially improved the model's predictive power and sensitivity.</p>
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series Proceedings of the International Association of Hydrological Sciences
spelling doaj-art-db9192593e3849b9b04736f2b4462c612025-08-20T02:12:34ZengCopernicus PublicationsProceedings of the International Association of Hydrological Sciences2199-89812199-899X2024-11-0138634535110.5194/piahs-386-345-2024Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression ApproachH. Zhong0D. Liang1D. Liang2Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL, 35401, USADepartment of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL, 35401, USACivil, Mechanical and Manufacturing Innovation (CMMI), National Science Foundation, Alexandria, VA, 22314, USA<p>This study investigated the probability of road closure due to flooding. Logistic regression model was developed using the road closure data and the daily rainfall data for Houston, TX, USA during 2017 and 2018. The road network was further divided into flood prone zones. The spatial analysis revealed that the rainfall at the road segment level could be sufficiently represented by that recorded by nearest sensors. Within a 4 d window, the rainfall in the current day and 3 d prior played a more influential role in predicting road closure. The differential outcomes due to distinct regional features were explained. Finally, a watershed delineation approach substantially improved the model's predictive power and sensitivity.</p>https://piahs.copernicus.org/articles/386/345/2024/piahs-386-345-2024.pdf
spellingShingle H. Zhong
D. Liang
D. Liang
Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach
Proceedings of the International Association of Hydrological Sciences
title Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach
title_full Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach
title_fullStr Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach
title_full_unstemmed Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach
title_short Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach
title_sort evaluating regional variability in road closure outcomes due to rainfall a logistic regression approach
url https://piahs.copernicus.org/articles/386/345/2024/piahs-386-345-2024.pdf
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