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All Journal Manajemen Informatika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jukema (Jurnal Kesehatan Masyarakat Aceh) Seminar Nasional Teknologi Informasi Komunikasi dan Industri IJISCS (International Journal Of Information System and Computer Science) bit-Tech PREPOTIF : Jurnal Kesehatan Masyarakat International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) International Research on Big-data and Computer Technology (IRobot) Prosiding Konferensi Nasional PKM-CSR COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Journal Of Safety and Health Jurnal Masyarakat Madani Indonesia Madani: Multidisciplinary Scientific Journal Enrichment: Journal of Multidisciplinary Research and Development SmartComp Multidisciplinary Indonesian Center Journal IJISCS (International Journal of Information System and Computer Science)
Leni Novianti
Politeknik Negeri Sriwijaya
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Comparative Analysis of LSTM and GRU for River Water Level Prediction Faris, Fakhri Al; Taqwa, Ahmad; Handayani, Ade Silvia; Husni, Nyayu Latifah; Caesarendra, Wahyu; Asriyadi, Asriyadi; Novianti, Leni; Rahman, M. Arief
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5054

Abstract

Accurate river water level prediction is essential for flood management, especially in tropical areas like Palembang. This study systematically analyzes the performance of two deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for real-time water level forecasting using hourly rainfall and water level data collected from automatic sensors. A series of experiments were conducted by varying window sizes (10, 20, 30) and the number of layers (1, 2, 3) for both models, with model performance assessed using RMSE, MAE, MAPE, and NSE. The results demonstrate that both window size and network depth significantly influence prediction accuracy and computational efficiency. The LSTM model achieved its highest accuracy with a window size of 30 and a single layer, while the GRU model performed best with a window size of 20 and two layers. This work contributes by systematically analyzing hyperparameter configurations of LSTM and GRU models on hourly rainfall and water level time series for flood-prone regions, offering empirical insight into parameter tuning in recurrent neural architectures for hydrological forecasting. These findings highlight the importance of careful parameter selection in developing reliable early warning systems for flood risk management.
Co-Authors Ade Silvia Handayani Ahmad Taqwa Asriyadi Asriyadi Devi Susanti Dewi Irmawati Siregar Dewi Ratna Sari Emilia Hesti Ervi Cofriyanti Fakhruddin, M Faris, Fakhri Al Febie Elfaladonna Indri Ariyanti Irzanita, Irzanita Isa, Indra Griha Tofik Kaila, Afifah Syifah Khoirunnisa, Dilla Marieska Lupikawaty Maulana, Nurehan Meiriska, Ienda Meriska, Ienda Minarti Minarti Minata, Fika Nofriyanti, Duwi Nyayu Latifah Husni, Nyayu Latifah Olsananda, Benedicta Vemrihany Purwanto, Medy Rahman, M. Arief Rahmatullah, Faiz Rakhman, M Arief Rakhman, M.Arief Rani, Resti Atika Rika Sadariawati Ripandi, Irfan Ririn Noviyanti Putri RIZCITA PRILIA MELVANI Rosadi, Hengky Siregar, Dewi Irmawati Suroso Suroso Sutriyati Suzan Agustri Taufikurrahman Taufikurrahman Tri Widiastuti Wahyu Caesarendra Zulkarnaini Zulkarnaini