ArrenOnom/FloodDynamics-XAI-Nigeria

A geospatial machine learning framework integrating Sentinel‑1 SAR, explainable AI (TreeSHAP), and hydrodynamic terrain indicators to evaluate pluvial and fluvial flood dynamics and population exposure across North‑Central Nigeria. # FloodDynamics-XAI-Nigeria ## Overview This repository hosts the research project **"Evaluating Pluvial and Fluvial Flood Dynamics in Data-Scarce Tropical Catchments: An Integrated Explainable AI (XAI) and Spaceborne SAR-Hydrodynamic Modeling Framework across North Central Nigeria"** by Joseph James Umar and Nelson Oyenbuchi Nwobi. The project integrates: - Sentinel-1 Synthetic Aperture Radar (SAR) imagery - Geospatial predictors (hydro-geomorphic, atmospheric, land surface) - Machine learning algorithms (Random Forest, XGBoost, LightGBM) - Explainable AI (TreeSHAP) - Population exposure mapping using WorldPop data The workflow provides a reproducible framework for flood susceptibility modeling, mechanism disentanglement, and risk assessment in tropical, data-scarce environments. --- ## Repository Contents - **FloodDynamics-XAI-Nigeria.ipynb** → The main Google Colab notebook containing the full workflow. - **FloodDynamics-XAI-Nigeria.ipynb** → Alternate copy of the notebook saved in the root folder for direct Colab use. - **/data** → CSV datasets and shapefiles (hosted in Google Drive, mounted in Colab). - **/scripts** → Supporting Python scripts for preprocessing and analysis. - **/results** → Outputs including hazard maps, model metrics, and exposure statistics. --- ## How to Use 1. Clone or download this repository: ```bash git clone github.com 2. Or open the **FloodDynamics-XAI-Nigeria.ipynb** in colab and follow the instructions