# Fostering Sustainable Aquaculture: Mitigating Fish Mortality Risks Using Decision Trees Classifiers
## 1 Introduction  
Increasing mortality rates are a major challenge for both wild fisheries and in marine aquaculture. Various factors, such as overfishing, the presence of pollutants, the destruction of ecosystems, and climate change, contribute to the increased rates. Overfishing threatens the sustainability of marine aquaculture, underscoring the urgent need to address this challenge [1; 2]. Furthermore, pollution from industrial and agricultural activities has resulted in a degraded water quality, adversely impacting fish health and survival [3]. In the realm of aquaculture, unsustainable practices, such as overcrowding, poor water quality, and excessive antibiotic use, can trigger disease outbreaks and lead to heightened mortality rates. The rapid expansion of aquaculture has been accompanied by environmental degradation and disease proliferation, resulting in substantial fish losses [4; 5]. This work introduces a project dedicated to enhancing Greek fish farming through the incorporation of innovative technologies and sustainable practices. The project's core objective is the development of intelligent systems for diagnosing and treating fish diseases, ultimately bolstering the competitiveness of Greek aquaculture. The proposed data-driven strategy utilizes data mining classification methods, including decision trees (DTs), to predict and categorize instances of fish mortality. The model considers various factors such as the geographical locations, husbandry methods, water quality, weather conditions, and biological characteristics of the fish. The application of DTs in the research allows for the identification of thresholds for features like the median atomic weight (MAB), water temperature (Temp), volume of the cell (Vol), and concentration of fish inside the cell (i-f), leading to the classification of outcomes into specific classes. The project's aim is to aid sustainable aquaculture management, offering a proactive approach to address fish mortality issues by demonstrating valuable insights into the factors influencing fish mortality. The findings aid the development of targeted conservation and management strategies, empowering stakeholders to protect farmed fish stocks. The current research complies with the UN sustainability goals to reduce seafood production losses due to climate change [6]. This research effort aims to promote sustainable fishing and aquaculture in Greece. The current project, entitled \"Improving Greek Fish Farming Competitiveness\", aims to upgrade the sector by creating an intelligent system for diagnosing fish diseases, taking into account factors including the treatment, diet, temperature, and volume. This initiative is coordinated with a broader effort to enhance the competitiveness of Greek fish farming through the integration of technological innovation and sustainable practices [7]. This study comprises six distinct sections, each contributing to a comprehensive understanding of the proposed data-driven strategy for fish mortality prediction and categorization. These sections encompass the Introduction, Related Work, Research Methodology, Results, Discussion, and Conclusions. Each segment provides a structured overview of our approach, methodology, findings, and implications. Hence, the introductory part has made clear the scope and goal of the current study regarding the application of data mining models in fish mortality prediction. Following this, the Related Work refers to existing research, studies, or projects that are relevant to the use of data mining in fish mortality prediction, incorporating the research objectives of the study. The Research Methodology mentions the specific approach, techniques, and procedures used to conduct the study. This section outlines how the research was designed, implemented, and analyzed to address the research questions or objectives. It includes details about the research scope, an overview of the method used, the dataset, as well as the DTs classifiers.