Climate-Driven Poverty Prediction in Nusa Tenggara Timur Using Transfer Learning-Based CNN-LSTM and ERA5-Land
DOI:
https://doi.org/10.63158/journalisi.v8i5.1707Keywords:
Climate-Poverty Prediction, Climate Risk Analytics, CNN-LSTM, Transfer Learning, ERA5-LandAbstract
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.
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