A geospatial machine-learning framework for regional electrification estimation mapping and trend analysis using demographic and infrastructure features
- 1Bahir Dar University
- 2University of Rwanda
Abstract
Many rural communities in Ethiopia’s Amhara region lack electricity access despite substantial urban-rural disparities. Traditional surveys are costly and inconsistent, while Nighttime Light (NTL)-population threshold methods suffer from arbitrary thresholds, off-grid detection failures, and light-access conflation. This study develops a geospatial machine learning framework using Visible Infrared Imaging Radiometer Suite—Day/Night Band (VIIRS-DNB) nighttime lights, population density, and distances to grid lines and roads. Random Forest (RF) outperforms Support Vector Machine (SVM) and Decision Tree (DT), achieving 86.8% overall accuracy and a 71.3% Kappa coefficient for operational mapping of electrification status and estimation of regional electrification rates from 2018 to 2024. Validation against GPS-recorded transformer locations demonstrates that the Random Forest model reconstructs a realistic, corridor-type electrified network and outperforms a calibrated NTL–population threshold approach, which tends to miss grid-connected rural corridors and overestimate urban brightness effects. The resulting maps reveal zone-level disparities and steady growth to 41% electrification by 2024, with population density (27.49% importance) emerging as the leading predictor among balanced variables. This framework supports SDG 7 planning and investment prioritization in underserved areas.
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BibTeX
@article{workineh2026geospatial,
title = {{A geospatial machine-learning framework for regional electrification estimation mapping and trend analysis using demographic and infrastructure features}},
author = {Tewodros Gera Workineh and Getachew Biru Worku and Tassew Tadiwos Zewdie},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62680-3},
url = {https://doi.org/10.1038/s41598-026-62680-3},
note = {CC BY},
}
RIS
TY - JOUR
TI - A geospatial machine-learning framework for regional electrification estimation mapping and trend analysis using demographic and infrastructure features
AU - Tewodros Gera Workineh
AU - Getachew Biru Worku
AU - Tassew Tadiwos Zewdie
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62680-3
UR - https://doi.org/10.1038/s41598-026-62680-3
AB - Many rural communities in Ethiopia’s Amhara region lack electricity access despite substantial urban-rural disparities. Traditional surveys are costly and inconsistent, while Nighttime Light (NTL)-population threshold methods suffer from arbitrary thresholds, off-grid detection failures, and light-access conflation. This study develops a geospatial machine learning framework using Visible Infrared Imaging Radiometer Suite—Day/Night Band (VIIRS-DNB) nighttime lights, population density, and distances to grid lines and roads. Random Forest (RF) outperforms Support Vector Machine (SVM) and Decision Tree (DT), achieving 86.8% overall accuracy and a 71.3% Kappa coefficient for operational mapping of electrification status and estimation of regional electrification rates from 2018 to 2024. Validation against GPS-recorded transformer locations demonstrates that the Random Forest model reconstructs a realistic, corridor-type electrified network and outperforms a calibrated NTL–population threshold approach, which tends to miss grid-connected rural corridors and overestimate urban brightness effects. The resulting maps reveal zone-level disparities and steady growth to 41% electrification by 2024, with population density (27.49% importance) emerging as the leading predictor among balanced variables. This framework supports SDG 7 planning and investment prioritization in underserved areas.
SN - 2045-2322
ER -