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| 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: | 15-Aug-2021 | Source: | IEEE internet of things journal, 15 Aug. 2022, v. 9, no. 16, p. 14366-14374 | 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 | Rights: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication Y. Wang, I. W. -H. Ho, Y. Chen, Y. Wang and Y. Lin, "Real-Time Water Quality Monitoring and Estimation in AIoT for Freshwater Biodiversity Conservation," in IEEE Internet of Things Journal, vol. 9, no. 16, pp. 14366-14374, 15 Aug.15, 2022 is available at https://dx.doi.org/10.1109/JIOT.2021.3078166. |
| Appears in Collections: | Journal/Magazine Article |
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|---|---|---|---|---|
| Wang_Real-Time_Water_AIoT.pdf | Preprint version | 2.17 MB | Adobe PDF | View/Open |
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