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Nyando Basin Flood Risk Prediction System: A Production-Grade, Explainable Machine Learning Framework for Ward-Level Flood Susceptibility Mapping in Kisumu County, Kenya — Revised Version 2.1

Domaine:

climategeospatial

Type de record:

software
Créateur:
Koe
Éditeur:
Zenodo
Hôte:avatar
Revised and corrected Version 2.1 of the Nyando Basin Flood Risk Prediction System. Corrections in this version: population updated to 161,000 residents of Nyando sub-county, training dataset confirmed as 2,308 real Google Earth Engine satellite observation points, primary model confirmed as GradientBoosting achieving AUC-ROC 0.9717, F1 0.9022, spatial CV 0.9727 plus or minus 0.004, test suite expanded to 41 pytest unit tests with GitHub Actions CI/CD. The system delivers real-time 72-hour ward-level flood susceptibility scores for five wards of Nyando sub-county. Built entirely on Android using Termux and Google Colab from Kisumu, Kenya. Live API: nyando-flood-api.onrender.c… Dashboard: nyando-flood-ai.vercel.app Repository: jameskoero/nyando-flood-ai

Visit

doi.orgzenodo.org

Languages

Kenyan Sign Language

Tags

Flood susceptibilityGradient boostingXGBoostSHAP explainabilityNyando basinKisumu CountyKenyaCHIRPS rainfallSentinel-1 SARSpatial-cross-validation+10

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcodeCopyright (C) 2026 James Onyango Koero. Licensed under CC-BY-4.0.http://rightsstatements.org/vocab/InC/1.0/