Comparative Analysis of Multi-Horizon Deep Learning for Water Quality Forecasting in Shrimp Ponds

Santi; Arna Fariza; Agus Indra Gunawan

doi:10.46604/peti.2026.16540

Authors

Santi Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia
Arna Fariza Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia https://orcid.org/0000-0001-7740-6859
Agus Indra Gunawan Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia

DOI:

https://doi.org/10.46604/peti.2026.16540

Keywords:

water quality, forecasting, deep learning, multi-horizon

Abstract

Accurate multi-step water-quality prediction is essential for proactive and sustainable aquaculture management. Existing studies often evaluate only limited deep learning (DL) architectures or forecasting horizons, making fair comparisons difficult. This study systematically compares seven DL architectures for forecasting pH, temperature, salinity, and dissolved oxygen (DO) using 6,741 records collected from shrimp ponds at seven Indonesian locations (2017–2025). All models are evaluated using the same preprocessing, optimization, and evaluation settings for forecasting horizons of 5, 10, 20 and 30 steps ahead. Prediction performance consistently decreases as the forecasting horizon increases. Bidirectional long short-term memory (BiLSTM) achieves the lowest root mean square error (RMSE) (2.18) and mean absolute error (MAE) (0.82) at horizon 5 (H5), although repeated experiments indicate that the observed differences are not statistically significant. Performance varies across longer forecasting horizons, with gated recurrent unit (GRU) achieving the lowest RMSE at H20 (2.69).

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