Advancing beyond the Tank: Machine Learning Techniques for Detecting Water Quality and Temperature Anomalies in Intelligent Aquatic Systems

This study aimed to enhance the Beyond the Tank system by integrating machine learning models for real-time anomaly detection in water quality and temperature data.Extensive sensor data was collected and preprocessed, leading to the development and evaluation of various machine-learning algorithms.The most effective model was integrated into the system, enabling continuous monitoring and real-time alerts for anomalies.The mobile app was also upgraded to support immediate notifications, improving user responsiveness to potential issues.The enhancements led to significant improvements in system efficiency, resource utilization, and user satisfaction.The findings demonstrate the potential of machine learning in advancing intelligent aquarium management, providing a solid foundation for future research and practical applications in this field.