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A probabilistic model for predicting road network disruption by integrating large-scale flood forecasts, topographic characteristics, and local sensor data


Authors: Cesar Yahia
Owners: Cesar Yahia
Resource type:Composite Resource
Created:Sep 20, 2018 at 1:13 a.m.
Last updated: Dec 08, 2018 at 7:21 a.m. by Cesar Yahia

Abstract

In this project, we aim to predict the impact of storm events on the road network disruption state. We propose a framework that integrates large-scale discharge forecasts from the national water model (NWM) with local topographic and road information. The framework relies on a probabilistic model that predicts the likelihood of road network disruption from NWM-HAND inundation maps and observed road disruptions from past storms. Thus, by assimilating observed road data and NWM-HAND predicted inundation impact, we aim to improve predictions on the anticipated road network disruption state for a particular flood.

Subject Keywords

GISWR2018

How to cite

Yahia, C. (2018). A probabilistic model for predicting road network disruption by integrating large-scale flood forecasts, topographic characteristics, and local sensor data, HydroShare, http://www.hydroshare.org/resource/45122a8fc2ea4bc984d370e5b543d3d9

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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Authors

The people or organizations that created the intellectual content of the resource.

Name Organization Address Phone Author Identifiers
Cesar Yahia The University of Texas at Austin

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