AI-powered decision support system for mariculture: Real-time fish mortality prediction with random forest

Abstract

Fish mortality is a significant issue in mariculture, affecting productivity and sustainability. Predicting mortality risk in real-time is crucial for improving decision making and operational efficiency in mariculture management. This paper presents the development of a real-time fish mortality risk prediction model, designed as part of a Decision Support System using the Random Forest machine learning algorithm. The innovative aspect of this study lies in the real-time processing of sensor data to deliver daily mortality risk predictions, allowing for immediate adjustments to management practices. This study integrates water quality parameters (seawater temperature, salinity, conductivity, chlorophyll-a, turbidity, and dissolved oxygen) monitored through a sensor network, with daily fish mortality records input by farmers. The Random Forest model predicted fish mortality risk across five levels with an overall accuracy of 78.6 % and precision exceeding 70 % for each level. The model's feature importance analysis highlights seawater temperature, salinity, and turbidity as key predictors of fish mortality risk. This system supports fish farmers and site managers in daily operational decision making, particularly regarding feed and labor management. Future improvements in data collection and continuous model updates are expected to enhance the accuracy and utility of the Decision Support System in mariculture management.