Development and Testing of an Autonomous Surface Vehicle for Real - Time Water Chemistry Monitoring in Freshwater Ecosystems
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Abstract
Water purity is essential for ecosystem health, pollution management, and public safety. Data collection delays, high costs, limited scalability, and poor real-time capabilities plague traditional water monitoring technologies. Static sensors and manual sampling are inefficient for large or dynamic water bodies. This paper suggests an IoT-enabled real-time water monitoring system to solve these challenges. Advanced sensors upload pH, temperature, turbidity, dissolved oxygen, and total dissolved solids data to the cloud for quick analysis. Multi-class categorisation using machine learning allows precise trend tracking and prediction. Mobile sensors provide more flexibility and dynamic coverage than static systems. Optimised hardware and cloud-based design decrease expenses and generate a predictive database for improved decision-making. Scalable, cost-effective technology promotes sustainable water management, pollution control, and environmental conservation, addressing gaps in prior techniques and helping global environmental objectives.
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