What question did this study set out to answer?

The aim is to develop a machine learning system for predicting flood susceptibility in the Nyando River Basin.

May 10, 2026Open Access

Nyando Basin Flood Risk Prediction System: A Production-Grade, Explainable Machine Learning Framework for Ward-Level Flood Susceptibility Mapping in Kisumu County, Kenya

JKJames KoeroGreat Lakes University of Kisumu

Key Points

The aim is to develop a machine learning system for predicting flood susceptibility in the Nyando River Basin.
Utilized XGBoost classifier with AUC-ROC of 0.94.
Trained on open satellite data including CHIRPS, NASA DEM, and Sentinel-1 SAR.
Developed a Python package with a FastAPI prediction endpoint and a React web dashboard.
Achieved an AUC-ROC of 0.94 indicating high prediction accuracy.
Constructed a complete framework including source code, training data, and model weights.
Ensured 100% compliance with Kenya DPA 2019 and GDPR standards.

Abstract

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.

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