VTechWorks staff will be away for the Memorial Day holiday on Monday, May 27, and will not be replying to requests at that time. Thank you for your patience.

Show simple item record

dc.contributor.authorZhang, Ruoyuen_US
dc.date.accessioned2018-08-04T08:01:00Z
dc.date.available2018-08-04T08:01:00Z
dc.date.issued2018-08-03en_US
dc.identifier.othervt_gsexam:15498en_US
dc.identifier.urihttp://hdl.handle.net/10919/84499
dc.description.abstractModelling surface runoff can be beneficial to operations within many fields, such as agriculture planning, flood and drought risk assessment, and water resource management. In this study, we built a data-driven model that can reproduce monthly surface runoff at a 4-km grid network covering 13 watersheds in the Chesapeake Bay area. We used a random forest algorithm to build the model, where monthly precipitation, temperature, land cover, and topographic data were used as predictors, and monthly surface runoff generated by the SWAT hydrological model was used as the response. A sub-model was developed for each of 12 monthly surface runoff estimates, independent of one another. Accuracy statistics and variable importance measures from the random forest algorithm reveal that precipitation was the most important variable to the model, but including climatological data from multiple months as predictors significantly improves the model performance. Using 3-month climatological, land cover, and DEM derivatives from 40% of the 4-km grids as the training dataset, our model successfully predicted surface runoff for the remaining 60% of the grids (mean R2 (RMSE) for the 12 monthly models is 0.83 (6.60 mm)). The lowest R2 was associated with the model for August, when the surface runoff values are least in a year. In all studied watersheds, the highest predictive errors were found within the watershed with greatest topographic complexity, for which the model tended to underestimate surface runoff. For the other 12 watersheds studied, the data-driven model produced smaller and more spatially consistent predictive errors.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectdata-driven modelling; surface runoff simulation; random forest; machine learning; Chesapeake Bayen_US
dc.titleAn evaluation of a data-driven approach to regional scale surface runoff modellingen_US
dc.typeThesisen_US
dc.contributor.departmentGeographyen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineGeographyen_US
dc.contributor.committeechairShao, Yangen_US
dc.contributor.committeememberShortridge, Julie Elizabethen_US
dc.contributor.committeememberEllis, Andrewen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record