A probabilistic model for predicting road network disruption by integrating large-scale flood forecasts, topographic characteristics, and local sensor data
| Authors: | |
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| Owners: | Cesar Yahia |
| Type: | Resource |
| Storage: | The size of this resource is 10.0 MB |
| Created: | Sep 20, 2018 at 1:13 a.m. |
| Last updated: | Dec 08, 2018 at 7:21 a.m. |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 2395 |
| Downloads: | 220 |
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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.
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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/
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