Repository logo
 

Influence of spatial variation in precipitation on artificial neural network rainfall-runoff model

Simple item page
dc.contributor.authorDozier, André, author
dc.contributor.authorColorado State University, publisher
dc.date.accessioned2020-02-12T17:22:53Z
dc.date.available2020-02-12T17:22:53Z
dc.date.issued2012
dc.description2012 annual AGU hydrology days was held at Colorado State University on March 21 - March 23, 2012.
dc.descriptionIncludes bibliographical references.
dc.description.abstractModeling rainfall-runoff processes is a very challenging task due to data collection, time, money, and technology constraints. Artificial neural networks (ANNs) are modeling tools that can quickly adapt and learn input-output relationships for many different engineering problems. An Elman-type recurrent ANN was trained to simulate observed streamflow for Fountain Creek at Pueblo, CO, using varying amounts of spatial precipitation information. Nine zones were originally delineated within the watershed draining to Fountain Creek at Pueblo based on estimated overland flow travel time. Five different spatially varying scenarios were modeled: scenarios containing 9 zones, 6 zones, 3 zones, 2 zones, and 1 zone. Each scenario was trained and simulated 100 times, each with randomly generated initial weights. Spatial variability in precipitation data allows the ANN to achieve better performance when simulating the training dataset. However, when applied to the validation and testing time periods, ANN performance generally decreases with additional spatially variability. In addition to exploring results of the ANN rainfall-runoff model, the application of geographical information systems to rainfall-runoff input processing is demonstrated.
dc.format.mediumborn digital
dc.format.mediumproceedings (reports)
dc.identifier.urihttps://hdl.handle.net/10217/201016
dc.identifier.urihttp://dx.doi.org/10.25675/10217/201016
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofHydrology Days
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.titleInfluence of spatial variation in precipitation on artificial neural network rainfall-runoff model
dc.title.alternativeHydrology days 2012
dc.title.alternativeAGU hydrology days 2012
dc.typeText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
CONF_HydroDays_Dozier_2012.pdf
Size:
1.43 MB
Format:
Adobe Portable Document Format
Download

Collections

Hydrology Days
Hydrology Days