Water quality monitoring and grade judgment system based on IGWOPSO-SVM algorithm

JIANG Dong; XIAO Maohua; ZHANG Haijun; ZHOU Junbo; ZHU Hong; WANG Xiaochan; CHEN Shuang

doi:10.7671/j.issn.1001-411X.202207034

Journal of South China Agricultural University > 2023 > 44(4): 638-648. > DOI: 10.7671/j.issn.1001-411X.202207034

JIANG Dong, XIAO Maohua, ZHANG Haijun, et al. Water quality monitoring and grade judgment system based on IGWOPSO-SVM algorithm[J]. Journal of South China Agricultural University, 2023, 44(4): 638-648. DOI: 10.7671/j.issn.1001-411X.202207034

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Water quality monitoring and grade judgment system based on IGWOPSO-SVM algorithm

1.
College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
2.
Jiangsu Agricultural Machinery Test and Appraisal Station, Nanjing 210017, China
3.
Jiangsu Shuangmu Measurement and Control Technology Co., Ltd., Zhenjiang 212300, China

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Received Date: July 17, 2022
Available Online: September 03, 2023
Published Date: May 10, 2023

Graphical Abstract

Abstract

Abstract

Objective
Water pollution monitoring is a prerequisite for water pollution prevention and control in watersheds. In order to achieve high accuracy of surface water quality monitoring and water body rating judgement, we designed a water quality monitoring and rating system based on IGWOPSO-SVM (Improved grey wolf optimizer particle swarm optimization-support vector machine) model.
Method
We selected sensor group, STM32F103 microcontroller, ESP8266WIFI wireless communication module to build a water quality monitoring system data processing module. The WIFI wireless communication transmitted the water quality data collected by data processing module to the server. We designed water quality monitoring system server interactive end, while developing water quality monitoring applet for real-time monitoring of water quality grade. Based on the improved particle swarm optimization (IPSO) and grey wolf optimizer (GWO), the IGWOPSO algorithm was proposed to optimize SVM algorithm, according to which the IGWOPSO-SVM water quality rating algorithm was proposed. The water quality rating effect of this system was verified by experiment based on 135 groups water quality data of Nanjing Xuanwu Lake, Jinchuan River and Jiangpu water source.
Result
Compared with SVM, the total sample classification accuracy of IGWOPSO-SVM water quality rating algorithm increased from 86.67% to 100.00%, with an increase of 13.33 percent. Compared with particle swarm optimization (PSO), the best adaptation degree of IGWOPSO algorithm increased from 86.80 to 99.20, with an increase of 14.29%.
Conclusion
This study solves the problems of low efficiency and low accuracy of traditional water body rating methods, and provides a method reference for accurate monitoring of surface water quality.

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References (34)

References

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