Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91943
Title: Real-time water quality monitoring and estimation in AIoT for freshwater biodiversity conservation
Authors: Wang, Y 
Ho, IWH 
Chen, Y 
Wang, Y 
Lin, Y 
Issue Date: 2021
Source: IEEE internet of things journal, 2021, Early Access, https://dx.doi.org/10.1109/JIOT.2021.3078166
Abstract: Deteriorating water quality leads to the freshwater biodiversity crisis. The interrelationships among water quality parameters and the relationships between these parameters and taxa groups are complicated in affecting biodiversity. 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 IoT unmeasurable 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. For the multivariate polynomial regression model of degree eight, the coefficients of determination results are 0.89, 0.78, 0.87, and 0.81 for NO3-N, BOD5, PO4, and NH3-N, respectively. The effectiveness and efficiency of the proposed systems and models were validated against laboratory results and the overall performance is acceptable with most of the prediction errors smaller than 0.2mg/L, which provides insights into how AIoT techniques can be applied to pollutant discharge monitoring and other water quality regulatory applications for freshwater biodiversity conservation.
Keywords: Artificial intelligence models
Freshwater biodiversity
Internet of Things
Top-10 crucial water quality parameters
Water quality monitoring
Water quality parameter estimation
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE internet of things journal 
EISSN: 2327-4662
DOI: 10.1109/JIOT.2021.3078166
Appears in Collections:Journal/Magazine Article

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