Assessing Rural Water Suitability for Small-Scale Aquaculture Using Proximal Policy Optimization: A Deep Reinforcement Learning Case Study from North Macedonia

Abstract

Water quality plays a vital role in aquaculture sustainability, particularly for sensitive freshwater species such as Salmo letnica, which require strict physicochemical conditions for survival. This study evaluates the suitability of household water sources from eight rural villages in North Macedonia for supporting Salmo letnica in home-based tank environments. Seven key water quality parameters were measured and compared against biological thresholds derived from ecological literature. A Deep Reinforcement Learning (DRL) agent, trained using the Proximal Policy Optimization (PPO) algorithm, was developed to classify the suitability of water samples based on these parameters. While the DRL model exhibited low precision and recall due to the limited sample size, it provided a framework for interpretability through reward dynamics and parameter correlations. Among the eight villages, Forino and Gradec were found to meet all critical biological thresholds, while Kamenjane and Vrapçishte were identified as marginally suitable. The remaining locations exhibited insufficient oxygen levels or excessive nutrient concentrations. These findings demonstrate the potential of AI-based classification models in supporting aquaculture planning and ecological risk assessment. Future work will focus on data expansion, reward function refinement, and field-level model deployment. Unlike traditional supervised classifiers, the DRL agent enables autonomous learning and decision-making without requiring large labeled datasets. This makes the approach suitable for real-time, remote water quality monitoring in resource-limited rural areas.