Ensemble Modelling for Predicting Fish Mortality

Abstract

This paper proposes a novel ensemble approach, integrating Artificial Neural Networks (ANNs), Symbolic Regression (SR), and Decision Trees (DTs), to predict fish mortality caused by infectious diseases. The intensifying global burden of fish diseases threatens the sustainability of aquatic ecosystems and the aquaculture industry, necessitating sophisticated modelling strategies for effective disease management and control. The proposed approach capitalizes on the non-linear data modelling strength of ANNs, the explanatory power of SR, and the decision-making efficiency of DTs, offering both predictive accuracy and interpretable insights. The architecture of the proposed ensemble method is developed in two stages. In the intermediate stage, an ANN is employed to learn the complex, non-linear interactions between various biological and environmental factors impacting fish health. Additionally, SR is applied to produce a symbolic equation that effectively maps the input variables to fish mortality rates. In the final stage, a DT model is included to enhance prediction performance by capturing decision rules from the data. This hybrid approach offers superior prediction performance while also revealing meaningful biological/environmental relationships that can guide preventive and reactive interventions in the management of fish health. We evaluate the developed models using extensive real-world datasets acquired from two large Greek fish-farming units, which encompass representative disease types. The results demonstrate that our ensemble approach significantly outperforms traditional standalone models developed in our recent previous work, achieving enhanced predictive accuracy, robustness, and interpretability. Overall, this research has far-reaching implications for improving disease predictions, facilitating optimal decision-making in aquaculture management, and contributing to the sustainability of global fish stocks.