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| Type: | Resource | |
| Storage: | The size of this resource is 7.9 MB | |
| Created: | Apr 03, 2026 at 7:30 p.m. (UTC) | |
| Last updated: | Jul 15, 2026 at 5:38 p.m. (UTC) | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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Abstract
Code accompanying Percich et al., 2026. "Upscaling sediment source prediction for watershed management"
Authors: Abigal Percich (1), Allen Gellis (2), James Fox (3), and Admin Husic (1)*
(1) Department of Civil and Environmental Engineering, Virginia Tech
(2) Department of Atmospheric, Oceanic & Earth Sciences, George Mason University
(3) Department of Civil Engineering, University of Kentucky
*Admin Husic, [husic@vt.edu] 9408 Prince William St., Occoquan Watershed Monitoring Laboratory, Virginia Tech, Manassas, VA 20110
This resource contains code (Python and MATLAB) to prepare the data, develop a multivariate random forest (MVRF) model, and apply the model.
Repository Structure:
1. Data Preparation: Delineates watersheds used in the model and analyzes meta-analysis data.
2. Model Development: Trains multivariate random forest (MVRF) model and conducts Shapley feature importance.
3. Model Application: Evaluates model applicability in new basins and applies the model.
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Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Science Foundation | CAREER: Dynamic connectivity: a research and educational frontier for sustainable environmental management under climate and land use uncertainty | 2438017 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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