{
  "abstract": "Poverty dynamics in Nusa Tenggara Timur (NTT), Indonesia, are influenced by climate variability, particularly rainfall deficits and drought, which increase household economic vulnerability. This study develops a transfer learning-based Hybrid CNN-LSTM model to predict the annual poverty headcount ratio (%) across 22 regencies/municipalities in NTT. ERA5-Land monthly total precipitation and annual regency-level poverty data from 2003–2024 were integrated, yielding 484 regency-year observations. To address limited local time-series data, the CNN was pretrained on long-term ERA5-Land climate sequences and transferred to the NTT task, while the LSTM and output layers were fine-tuned using local data. The model achieved R² = 0.87 and outperformed standalone CNN and LSTM models. A 6–12-month lagged association between rainfall deficits and subsequent poverty changes was also identified, suggesting a potential anticipatory climate signal rather than direct causality. The findings highlight the potential of integrating climate reanalysis, transfer learning, and deep learning for poverty-risk prediction in data-constrained regions. However, further operational, institutional, and external validation is required before deployment for decision support.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Yampi R Kaesmetan"
    },
    {
      "name": "Rahmat Gernowo"
    },
    {
      "name": "Budi Warsito"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dmpgqtptcnn3n.bib",
  "cited_by": 0,
  "doi": "10.63158/journalisi.v8i5.1707",
  "fulltext_url": "https://journal-isi.org/index.php/isi/article/view/1707",
  "id": "dmpgqtptcnn3n",
  "issns": [
    "2656-5935",
    "2656-4882"
  ],
  "landing_url": "https://doi.org/10.63158/journalisi.v8i5.1707",
  "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",
      "QA1-939"
    ],
    [
      "lcc",
      "QA75.5-76.95"
    ],
    [
      "openalex",
      "1105|Climate change impacts on agriculture"
    ]
  ],
  "openalex": "W7223319784",
  "pdf_url": "https://journal-isi.org/index.php/isi/article/download/1707/1024",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "Asosiasi Doktor Sistem Informasi Indonesia",
  "ris": "https://research.draconitic.com/paper/dmpgqtptcnn3n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:47Z",
      "native_id": "cacf989597e145c2a9d188056c039152",
      "source": "doaj",
      "url": "https://doi.org/10.63158/journalisi.v8i5.1707"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1105|Climate change impacts on agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "Climate-Driven Poverty Prediction in Nusa Tenggara Timur Using Transfer Learning-Based CNN-LSTM and ERA5-Land",
  "title_text": "Climate-Driven Poverty Prediction in Nusa Tenggara Timur Using Transfer Learning-Based CNN-LSTM and ERA5-Land",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:47Z",
  "url": "https://research.draconitic.com/paper/dmpgqtptcnn3n",
  "venue": "Journal of Information Systems and Informatics",
  "versions": []
}