Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...

Combining artificial and human intelligence to improve mountain flood prediction: Code

Owners: This resource does not have an owner who is an active HydroShare user. Contact CUAHSI ( for information on this resource.
Type: Resource
Storage: The size of this resource is 13.2 MB
Created: Jan 09, 2018 at 8:46 p.m.
Last updated: May 14, 2018 at 12:04 a.m.
Citation: See how to cite this resource
Sharing Status: Public
Views: 1261
Downloads: 59
+1 Votes: Be the first one to 
Comments: No comments (yet)


Testing ogh and pyDHSVM for the Sauk Watershed.

Subject Keywords

Deleting all keywords will set the resource sharing status to private.



Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
University of Washington eScience Institute Winter Incubator 2018
National Science Foundation PREEVENTS TRACK 2: Integrated Modeling of Hydro-Geomorphic Hazards: Floods, Landslides and Sediment 1663859
Sauk Suiattle Indian Tribe
Bureau of Indian Affairs


People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Amanda Lehr University of Washington

How to Cite

Shivraj, P., C. Bandaragoda (2018). Combining artificial and human intelligence to improve mountain flood prediction: Code, HydroShare,

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


There are currently no comments

New Comment