Nyando Basin Flood Risk Prediction System: A Production-Grade, Explainable Machine Learning Framework for Ward-Level Flood Susceptibility Mapping in Kisumu County, Kenya
A production-grade machine learning flood susceptibility system for the Nyando River Basin, Kisumu County, Kenya. XGBoost classifier achieving AUC-ROC 0.94 trained on open satellite data (CHIRPS, NASA DEM, Sentinel-1 SAR). Includes modular Python source package, 19 pytest unit tests, FastAPI prediction endpoint, and React web dashboard. 100% open data — Kenya DPA 2019 and GDPR compliant. Full codebase, training data, model weights, and unit tests published under MIT and CC-BY-4.0 at github.com/jameskoero/nyando-flood-ai.
Authors
- James Koero (ORCID: https://orcid.org/0009-0001-8314-2682)
Institutions
- Great Lakes University of Kisumu (KE)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-05-08
- DOI
- https://doi.org/10.5281/zenodo.20088662
- Primary Topic
- Flood Risk Assessment and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00