Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Proceedings of the International Association of Hydrological Sciences

DOI: 10.5194/piahs-386-345-2024

Authors: Hao Zhong, Daan Liang

Institutions: University of Alabama, U.S. National Science Foundation, Division of Civil, Mechanical & Manufacturing Innovation

Language: English

Topic: Flood Risk Assessment and Management

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $37