Cloud-Enabled Mobile App Architecture for Water Quality Monitoring Utilizing Machine Learning Algorithms

This study presents an innovative framework for a water quality monitoring system, emphasizing the integration of cloud-enabled mobile applications with advanced Machine Learning (ML) algorithms. In our approach, sensors transmit real-time data to a cloud-based platform via a central gateway, facilitating dynamic data storage and accessibility through a dedicated mobile application. The system incorporates cutting-edge ML algorithms for data analysis and prediction, enhancing accuracy and effectiveness. Leveraging cloud computing infrastructure ensures seamless scalability and accessibility. The experimental setup includes the design and implementation of the mobile application, featuring functionalities for data visualization, user interaction, and real-time alerts. This framework demonstrates the potential of cloud-enabled mobile applications in augmenting ML algorithms for various applications, notably in water quality monitoring and environmental management.