# Ensemble Modelling for Predicting Fish Mortality +
## 2 Related Work  
The individual application of ANNs, SR models, and DTs constitutes a common ground in various domains; however, the conjunctive use of these Machine Learning (ML) models--as implemented in this work--is still relatively scarce in the literature. In this section, we present a representative collection of such conjunctive implementations. The first work that we discuss is [12], in which an ANN-based architecture for SR is proposed, which is integrated with other Deep Learning architectures, such that the whole system can be trained end-to-end through backpropagation. The authors demonstrated the performance of their model on several substantially different tasks and showed that it can extrapolate quite well outside of the training dataset compared to a standard ANN-based architecture. In [13], a novel hybrid model that combines SR and a deep Multilayer Perceptron (MLP) for one-month-ahead photovoltaic-power forecasting is proposed. The system was evaluated in a case-study analysis, using a real Australian weather dataset, where the employed input features were solar irradiation and the historical photovoltaic power data. The overall study revealed favourable properties of the proposed hybrid SR-MLP approach. In a similar vein, the authors of [14] used ANNs and SR to predict the energy consumption in public buildings at the University of Granada. Their study concluded that there are no significant differences in accuracy considering both techniques in the problems addressed. In [15], a collection of optimization studies was performed for helically finned tubes in heat exchangers, in the context of which a comparison of ANNs and SR-based correlations was conducted, whereas, in [16], an ANN and an SR model were conjunctively employed and revealed a completely new criterion for predicting glass-forming ability, a property that represents the difficulty of forming metallic glasses. Furthermore, the authors of [17] developed and evaluated an ANN and an SR model to predict instantaneous exhaust emission in transient conditions of a diesel engine, fuelled with animal fat in different proportions. Lastly, in [18], a framework based on SR, coupled with physics-informed neural networks, was proposed (physics-informed neural networks constitute a special type of ANNs that can embed the knowledge of any physical laws that govern a given dataset in the learning process and can be described by partial differential equations [19; 20]). The framework was tailored to uncover the unknown parts of non-linear equations of motion directly from data. As far as the use of DTs along with ANNs and/or SR is concerned, the authors of [21] introduced PS-Tree, an advanced ML algorithm that combines DTs with SR to handle non-linear, piecewise models efficiently. This method enhances traditional DTs by incorporating adaptive and evolutionary strategies to optimize feature construction and partition schemes dynamically. The success of PS-Tree in outperforming other state-of-the-art algorithms in a plethora of datasets underlines its effectiveness in handling complex modelling tasks. The use of SR to enhance DTs has also been explored in [22], where a novel technique that employs SR to enrich DTs splits was presented. The proposed method synergistically combines SR and DTs in order to discover short, compact trees, with much richer class decision boundaries. In [23], the integration of DTs and ANNs in learning-to-rank tasks for personal search applications is explored. The study introduces an ensemble method to leverage the strengths of both models, aiming to improve ranking effectiveness and deployment efficiency in personal search systems. Lastly, [PERSON] et al., in [24], discuss the challenges and solutions associated with multivariate time-series classification, particularly emphasizing the balance between interpretability and accuracy in ML models. Motivated by these remarks, the authors propose a hybrid model that integrates the strengths of both ANNs and temporal DTs.