Water quality prediction systems using IoT and Machine Learning: A systematic review

Abstract

This systematic review analyzes 52 studies (2018–2026) on integrated IoT and Machine Learning systems for water quality prediction in aquaculture. Results show that Random Forest achieves accuracy above 94 % in survival classification, while hybrid Deep Learning models such as CNN-SA-BiSRU reach R² = 0.9765 for dissolved oxygen prediction. The most monitored parameters are pH (98.2 %), temperature (92.9 %), and dissolved oxygen (62.5 %). Critical gaps are identified: scarcity of species-specific datasets for larvae and fry, predominance of single target models, and limited validation under real production conditions. The review concludes that IoT-ML integration has demonstrated technical feasibility, but further applied research is required for effective adoption in commercial aquaculture farms, particularly in resource constrained rural settings where most small-scale fish farming occurs.