Optimalisasi Akurasi Prediksi Curah Hujan Bulanan Menggunakan Deep Learning
DOI:
https://doi.org/10.47065/bulletincsr.v5i5.735Keywords:
Rainfall Prediction; RNN; GRU; Deep Learning; Lampung; HydrometeorologyAbstract
The Province of Lampung exhibits high rainfall variability influenced by various atmospheric dynamics such as the Asian Monsoon, Australian Monsoon, El Niño–Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD). Accurate rainfall prediction is crucial across multiple sectors, including agriculture, water resource management, and hydrometeorological disaster mitigation. However, prediction methods commonly used in the region are still dominated by statistical approaches or conventional machine learning techniques, which often struggle to capture long-term temporal patterns in rainfall data. On the other hand, deep learning technologies such as the Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) offer better capabilities in modeling time series data, yet no specific comparative evaluation has been conducted for rainfall prediction in the Lampung Province. Comparing these two methods is important because the architectural characteristics of RNN and GRU differ in handling long-term dependencies, and selecting the right model can directly impact prediction accuracy and the effectiveness of decision-making in affected sectors. This study aims to implement and compare the performance of RNN and GRU in predicting monthly rainfall in Lampung Province using data from 80 rain gauges distributed across 15 districts/cities over the period from January 1991 to February 2025. The results show that the RNN model outperforms the GRU model, with lower RMSE (115.61 vs. 119.50), smaller MAE (86.94 vs. 91.28), and higher R² (0.35 vs. 0.30). Predictions for the period from March 2025 to February 2026 reveal a clear seasonal pattern, with minimum rainfall occurring in August 2025 (peak dry season) and maximum rainfall in January 2026 (peak rainy season). This study demonstrates that RNN is more effective than GRU in capturing the temporal patterns of rainfall, making it more recommended for long-term prediction applications.
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