基于IGWOPSO-SVM算法的水质监测及等级评定系统

目的 水污染监测是流域水污染防治工作的前提。为实现高精度的地表水水质监测及水体等级评定，本研究设计基于IGWOPSO-SVM(Improved grey wolf optimizer particle swarm optimization-support vector machine)模型的水质监测及等级评定系统。方法 选用传感器组、STM32F103单片机、ESP8266WIFI无线通信模块搭建水质监测系统数据处理模块，利用WIFI无线通信将数据处理模块采集到的水质数据传输至服务器，设计水质监测系统服务器交互端，同时开发水质监测小程序对水质等级进行实时监测。基于改进粒子群算法(Improved particle swarm optimization，IPSO)及灰狼算法(Grey wolf optimizer，GWO)提出了IGWOPSO算法，对SVM进行优化，据此提出了IGWOPSO-SVM水质等级评定算法。基于南京市玄武湖、金川河、江浦水源地135组水质数据对本系统水质等级评定效果进行试验验证。结果 相比于SVM，IGWOPSO-SVM水质等级评定算法的总样本分类准确率由86.67%上升至100.00%，上升了13.33个百分点；相比于粒子群算法(Particle swarm optimization，PSO)，IGWOPSO算法的最佳适应度由86.80上升至99.20，提高了14.29%。结论 本研究解决了传统水体等级评定方法效率低、准确率低的问题，为地表水水质的精确监测提供了方法借鉴。

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

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.