Checking for missing content type metadata ...
This resource contains content types with missing metadata required to make it public or discoverable. Show missing content type metadata.
Click on the edit button ( ) below to edit this resource.
Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...
This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.
This resource contains content types with files that need to be updated to match with metadata changes. Show content type files that need updating.
A probabilistic model for predicting road network disruption by integrating large-scale flood forecasts, topographic characteristics, and local sensor data
| Authors: |
|
|
|---|---|---|
| Owners: |
|
This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource. |
| Type: | Resource | |
| Storage: | The size of this resource is 10.0 MB | |
| Created: | Sep 20, 2018 at 1:13 a.m. (UTC) | |
| Last updated: | Dec 08, 2018 at 7:21 a.m. (UTC) | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
|---|---|
| Views: | 4254 |
| Downloads: | 318 |
| +1 Votes: | Be the first one to this. |
| Comments: | No comments (yet) |
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
Content
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
Comments
There are currently no comments
New Comment