Percich-et-al-2026 CODE: Sediment fingerprinting (sourcing) using machine learning
收藏资源简介：
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.
