A probabilistic model for predicting road network disruption by integrating large-scale flood forecasts, topographic characteristics, and local sensor data
|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|
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.
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This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/
|Cesar Yahia||The University of Texas at Austin|
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