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One Of The Finite Resource As Essential To Agriculture And Industry As Well As Basic Human Existence Is Freshwater. Water Quality Monitoring Is An Important Tool In The Management Of Freshwater Resources. Never-theless, Due To The Limited Types Of Internet Of Things (IoT) Sensors Available On The Market, A Large Number Of Chemical And Biological Parameters Still Rely On Laboratory Tests. With The Latest Advancement In Artificial Intelligence And The IoT (AIoT), This Technique Can Be Applied To Real-time Monitoring Of Water Quality, And Further Conserving Biodiversity. In This Paper, We Conducted A Comprehensive Literature Review On Water Quality Parameters That Impact The Biodiversity Of Freshwater And Identified The Top-10 Crucial Water Quality Parameters. Among These Parameters, The Interrelationships Between The IoT Measurable Parameters And IoTunmeasurable Parameters Are Estimated Using A General Regression Neural Network Model And A Multivariate Polynomial Regression Model Based On Historical Water Quality Monitoring Data. Conventional Field Water Sampling And In-lab Experiments, Together With The Developed IoT-based Water Quality Monitoring System Were Jointly Used To Validate The Estimation Results Along An Urban River In Hong Kong. The General Regression Neural Network Model Can Successfully Distinguish The Abnormal Increase Of Parameters Against Normal Situations.

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