Application of High-Frequency Intelligent Sensing Network in Monitoring and Early Warning of Water Quality Dynamic Change

Abstract

Abstract By implementing a high-frequency intelligent network of sensors, this work explores continuous monitoring and alerting for dynamic changes in water quality. Life depends on water, yet pollution is a greater menace. For this reason, precautions and careful observation are necessary. Typically, the focus on conventional water quality system monitoring is too much on data collection and needs more on analysis and extraction, limiting its capacity to offer thorough solutions. Making informed decisions becomes more complicated when there are discrepancies like damaged data, loss from power outages, or transmission issues. The proposed High-Frequency Intelligent Sensing Network (HFISN) monitoring system uses cloud computing, IoT and Big data technologies for intelligent sensing. Researchers developed it to address various challenges. Researchers recommend Nephelometric Turbidity Unit (NTU) Sensor installation to enhance the system’s performance and facilitate better monitoring of sedimentation, particle issues, and water purity. This sensor makes it possible to make more informed decisions by expanding the platform’s dataset. The solution not only resolves data cleaning and analysis issues but also includes intelligent early-warning capabilities for timely alerts. Quantum Cloud (QC) technology is employed to enhance security and accessibility. Test findings confirm its robustness with extra features and a built-in turbidity sensor. Because the platform ensures data accuracy and dependability, it provides decision-makers with a solid foundation to protect water resources.

Keywords

• Quantum cloud data analysis
• Sensor
• Big data
• Nephelometric turbidity unit
• High-frequency intelligent sensing network
• Water quality monitoring