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  1. Artikel Jurnal

    Global temperature anomaly prediction by using additive twin LSTM networks

    Cemal Keles; Burhan Baran; Baris Baykant Alagoz

    Scientific Reports, Vol. 16 No. 1 · 2026 · Inggris · DOAJ Scopus

    Abstract Due to the complexity of climate systems, data-driven modeling based on observed time series data is essential for predicting future climatic trends. This study aims to improve the long-term global temperature anomaly forecast performance of Long Short-Term Memory (LSTM) based neural…