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Multiparameter Prediction of Water Quality using Edge Intelligence

International Journal of Electronics and Communication Engineering
© 2023 by SSRG - IJECE Journal
Volume 10 Issue 5
Year of Publication : 2023
Authors : Aleefia A. Khurshid, Anushree Mrugank Minase, Ashlesha Bonkinpelliwar
10.14445/23488549/IJECE-V10I5P117
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How to Cite?

Aleefia A. Khurshid, Anushree Mrugank Minase, Ashlesha Bonkinpelliwar, "Multiparameter Prediction of Water Quality using Edge Intelligence," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 5, pp. 178-186, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I5P117

Abstract:

The ecosystem and public health are significantly threatened by water contamination. Contaminated water can have harmful consequences; therefore, monitoring water quality has become a pressing environmental concern. This work proposes a multiparameter water pollutant prediction model to ensure a green environment. The contribution is towards the use of fewer inputs to predict multiple parameters. Here Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and nitrate levels in the water are predicted using the Extra Trees algorithm. A modular network is implemented to ensure enhanced efficiency and independent training, while Principal Component Analysis (PCA) aids in reducing the data load and improving the response time. The learning algorithm uses minimal sensed parameters, such as temperature, pH, and DO conductivity and is cost-effective in computation and simple to integrate into an IoT hardware system, thus reducing the need for expensive online sensors and environment dependency. The results indicate enhanced efficiency with a maximum error of 10%. Nash-Sutcliffe Efficiency (NSE) for the proposed model is 0.9. The pruned model is also implemented on Raspbian OS to integrate the developed soft sensor in an IoT environment, and the prediction timing is 5.48 seconds with a CPU clock speed of 1.2GHz.

Keywords:

Water quality prediction, Machine learning, Extra trees algorithm, Soft sensors, Principal component analysis.

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