Ali-Abousaeidi/global-sensitivity-analysis-random-forest-SWATplus

Comprehensive repository for Global Sensitivity Analysis of SWAT+ model inputs in the Simiyu River Basin, Tanzania. Includes hydrological data (1952–2019), Random Forest models, and results. # Global Sensitivity Analysis Using Random Forest: An Application to SWAT+ This repository contains all resources for the research article: **Title:** Global Sensitivity Analysis of Complex Numerical Models Using Random Forest: An Application to SWAT+ **Authors:** Ali Abousaeidi, Seyed Mohammad Mahdi Moezzi, Farkhondeh Khorashadi Zadeh, Razi Sheikholeslami, Albert Nkwasa, Paul Munoz, and Ann van Griensven --- ## Repository Contents - **`data/`**: Contains the datasets used in the study (e.g., meteorological data and SWAT+ outputs). - **`code/`**: Contains scripts and Jupyter Notebooks for Random Forest-based GSA. - **`README.md`**: Overview of the project. - **`data/README.md`**: Detailed descriptions of datasets. - **`code/README.md`**: Instructions for using the code. --- ## Study Overview This study applies a Random Forest-based surrogate model to perform Global Sensitivity Analysis (GSA) on the SWAT+ model. The GSA identifies and ranks the most influential input variables for simulating river flow and sediment load in the Simiyu River Basin, Tanzania. Key features of the study: - **Case Study**: Simiyu River Basin, located in Southeast Lake Victoria, Tanzania. - **Data Period**: January 1, 1952 – December 1, 2019 (monthly resolution). - **Key Methods**: - Permutation Variable Importance (PVI) measures. - Partial Dependence Plots (PDPs). --- ## How to Use This Repository 1. Clone this repository: ```bash git clone github.com