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. | |
Last updated: | Dec 08, 2018 at 7:21 a.m. | |
Citation: | See how to cite this resource |
Sharing Status: | Public |
---|---|
Views: | 2008 |
Downloads: | 210 |
+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