Predictive modelling of aquaculture water quality using IoT and advanced machine learning algorithms - Md. Abdullah Al Mamun Hridoy; Chiara Bordin; Andleeb Masood; Khalid Masood (Results in Chemistry)
Aquaculture plays a pivotal role in global food security, with tilapia (Oreochromis niloticus) being one of the most widely farmed species due to its resilience and productivity. However, maintaining optimal water quality remains a key challenge, particularly in rural aquaculture systems with limited access to real-time monitoring tools. This study presents a comprehensive six-month monitoring of key water quality parameters in tilapia ponds in Montería, Colombia, using a custom-built Internet of Things (IoT) system. The parameters monitored include pH, turbidity, temperature, and dissolved oxygen (DO)—critical indicators of aquatic health and fish productivity. Advanced machine learning models, including TensorFlow Neural Networks (TFN) and Aqua Enviro Index (AEI), were applied for predictive analysis. Results revealed a statistically significant regression model for temperature (p < .001) and a weak negative correlation between turbidity and temperature (r = −0.093), highlighting the complex interactions within tropical aquaculture systems. The study offers valuable insights into temporal water quality dynamics and supports data-driven water quality management in resource-constrained areas. Future applications may involve developing mobile dashboards for real-time farmer alerts and decision support, alongside localized training to enhance data literacy. These initiatives can significantly improve aquaculture sustainability, foster technological adoption, and contribute to global food security by empowering rural fish farming communities.