{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Bahir Dar University",
    "University of Rwanda"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tewodros Gera Workineh"
    },
    {
      "affs": [
        1
      ],
      "name": "Getachew Biru Worku"
    },
    {
      "affs": [
        0
      ],
      "name": "Tassew Tadiwos Zewdie"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f75wj8w7eh6q2.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-62680-3",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-62680-3",
  "id": "f75wj8w7eh6q2",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-62680-3",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2306|Impact of Light on Environment and Health"
    ]
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  "openalex": "W7169768984",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62680-3_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/f75wj8w7eh6q2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:42Z",
      "native_id": "36ae27404e1d4173b27276381a64fad3",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-62680-3"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2306|Impact of Light on Environment and Health",
      "subfield": "climate"
    }
  ],
  "title": "A geospatial machine-learning framework for regional electrification estimation mapping and trend analysis using demographic and infrastructure features",
  "title_text": "A geospatial machine-learning framework for regional electrification estimation mapping and trend analysis using demographic and infrastructure features",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:42Z",
  "url": "https://research.draconitic.com/paper/f75wj8w7eh6q2",
  "venue": "Scientific Reports",
  "versions": []
}