Abstract
In the rapidly expanding field of aquaculture, ensuring the welfare of fish is essential for sustainability and productivity. Recognizing how environmental conditions and operational practices impact fish health, there is a need for robust methodologies that can predict and assess fish welfare effectively. This paper introduces a systematic modeling approach designed to advance fish welfare assessment by integrating diverse data sources including historical, environmental, operational data, and time-series analysis into a cohesive analytical framework. The proposed model focuses on developing and implementing inference rules that extract meaningful insights from heterogeneous datasets collected from multiple sources, such as direct observations, sensor outputs, and operational logs. This integration facilitates the use of machine learning techniques to assess and predict the welfare of fish groups. Time-series data provide a baseline for evaluating current conditions, while environmental and operational data contribute to realtime welfare assessments. In addition, the model features the construction of a fish welfare journal that significantly enhances its reasoning capabilities by providing deeper insights into the welfare status of fish over time. The utility of the model is demonstrated through its ability to predict and forecast welfare scores based on changes in environmental and operational conditions, which is vital for effective decision making, planning, and scheduling in fish farming operations. We showcase the practical applications of our model with a numerical example that illustrates its use in a real-world scenario. This example highlights how integrated data and inference methodologies can be employed to deduce welfare scores and make informed decisions about fish farming practices. Taking this example further, we train a predictive model to forecast welfare scores, illustrating the benefits of the proposed model, which not only dynamically calculates welfare scores, but also integrates time-series historical data to assess the performance of fish farming operations. Overall, this work not only deepens our understanding of fish welfare dynamics but also helps establish a more refined and robust welfare assessment protocol, extending the predictive capabilities into future operational planning and management.