IoT-driven ensemble machine learning model for accurate dissolved oxygen prediction in aquaculture

As the world demands sustainable protein sources the global food supply increasingly depends on aquaculture activities. Maintaining optimal water quality continues to pose ongoing difficulties. Fish health and production levels depend heavily on temperature control combined with proper pH values along with Dissolved Oxygen (DO) standards for optimal performance. Current water quality monitoring operations require long durations of manual work and delayed data analysis which reduces the speed of timely responses. The proposed research implements Internet of Things (IoT) technology along with ensemble Machine Learning (ML) model to improve DO and Water Quality Index (WQI) prediction accuracy. A series of IoT enabled sensors operated at 15-minute interval across multiple days gathered extensive dataset which measured temperature and pH levels and DO content. A comparison of five machine learning prediction methods which included Polynomial Regression and Support Vector Regression (SVR), Random Forest and Gradient Boosting and XGBoost is presented in this article. The proposed ensemble model achieved exceptional results because it generated $$R^2$$ values of 0.84 to 0.86 for DO measurements and WQI calculations approaching 0.91. The model’s ability to detect non-linear patterns enables exact real-time predictions which enable quick proactive decisions that minimize fish mortality rates. Advanced machine learning when combined with IoT technology enables improved operational efficiency along with sustainability enhancements. The proposed methodology optimizes water management systems for Catla and Tilapia fish farmers to enhance yield production while promoting an environmentally sustainable aquaculture industry.