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Tukan, Tobias T
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Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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PREDIKSI CURAH HUJAN HARIAN KABUPATEN MANOKWARI BERBASIS LONG SHORT-TERM MEMORY Padang, Eohansen; Subgan, Aries A.; Kardiputra, Kwasti K.; Tukan, Tobias T
Jurnal Natural Vol. 21 No. 1 (2025): Jurnal Natural
Publisher : FMIPA Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30862/jn.v21i1.293

Abstract

An accurate daily rainfall prediction in tropical regions such as Manokwari Regency, West Papua Province, is critically important yet challenging due to high rainfall variability and complex climatic influences, including ENSO, IOD, and orographic effects. This study develops a Long Short-Term Memory (LSTM) model for daily rainfall prediction in Manokwari Regency, utilizing bias-corrected historical data from BMKG (2020-2024) and reanalysis ERA5 ECMWF Data (2005-2024) through Random Forest correction. Four LSTM architectures (Vanilla LSTM, Stacked LSTM, Hybrid CNN-LSTM, and Bidirectional LSTM (BiLSTM)) were evaluated with optimized hyperparameters (window size 30/60 days, batch size 32/64, learning rate 0.001), assessed using root mean squared error (RMSE) and mean absolute error (MAE) metrics. Results demonstrate that the BiLSTM model with two layers (64-32 nodes) and 60-day window size achieved superior performance (RMSE 10.55 mm/day, MAE 6.49 mm/day) compared to other architectures. While the LSTM model effectively captured seasonal rainfall patterns, deviations occurred during extreme events, potentially due to limitations in modeling long-term rainfall dynamics. These findings suggest LSTM's strong potential for early warning systems and water resource management in high-variability rainfall regions.
Co-Authors Kardiputra, Kwasti K. Padang, Eohansen Subgan, Aries A.