Machine Learning-Based Framework for Sustainable Aquaculture Environment Management and Regulatory Monitoring
Keywords:
Sustainable Aquaculture, Machine Learning, Internet of Things (IoT), Water Quality Monitoring, Environmental Prediction, Regulatory Compliance, Smart Aquaculture, Artificial Intelligence, Decision Support System.Abstract
Sustainable aquaculture plays a crucial role in meeting the growing global demand for aquatic food while minimizing environmental degradation and ensuring regulatory compliance. However, conventional aquaculture monitoring methods rely on periodic inspections and manual data collection, making them inefficient for detecting rapid changes in water quality and environmental conditions. This study proposes a Machine Learning-Based Framework for Sustainable Aquaculture Environment Management and Regulatory Monitoring that integrates Internet of Things (IoT)-enabled sensing, intelligent data preprocessing, machine learning-based predictive analytics, automated regulatory compliance assessment, and real-time decision support into a unified architecture. The proposed framework continuously monitors critical environmental parameters, including temperature, dissolved oxygen, pH, turbidity, and ammonia, to predict water quality deterioration, identify environmental risks, and generate timely alerts for corrective actions. Performance evaluation demonstrates that the framework achieves an average water quality prediction accuracy of 97.8%, environmental risk detection F1-score of 97.6%, regulatory compliance rate of 98.4%, and operational efficiency of 96.9%, while reducing decision response time from 35 minutes to 6 minutes compared with conventional monitoring approaches. The integrated compliance monitoring module further enhances transparency by automating environmental reporting and regulatory verification. The proposed framework provides an intelligent, scalable, and reliable solution for next-generation smart aquaculture systems by improving environmental sustainability, resource utilization, operational productivity, and regulatory governance. It offers a practical foundation for deploying AI-driven monitoring technologies that support environmentally responsible and economically sustainable aquaculture development.