结合光谱降维的IPSO-SVR水体总磷浓度预测模型

王彩玲; 张国浩

doi:10.13961/j.cnki.stbctb.2024.02.021

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结合光谱降维的IPSO-SVR水体总磷浓度预测模型

试验研究

- 结合光谱降维的IPSO-SVR水体总磷浓度预测模型
- Predictive Model of Total Phosphorus Concentration in IPSO-SVR Waters Combined with Spectral Downscaling
- 水土保持通报 2024年44卷第2期页码：196-204
- 作者机构：
  
  西安石油大学计算机学院,陕西,西安,710065
- 作者简介：
- 基金信息：
  
  陕西省重点研发计划项目“结合深度学习的高光谱水质监测模型研究”(2023-YBSF-437);国家自然科学基金(61401439;41301382;31160475;62002286;62276213)
- DOI：10.13961/j.cnki.stbctb.2024.02.021
  中图分类号： 131.2;P237;O657.3
- 纸质出版：2024
- 稿件说明：
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王彩玲, 张国浩. 结合光谱降维的IPSO-SVR水体总磷浓度预测模型[J]. 水土保持通报, 2024,44(2):196-204.

Wang Cailing, Zhang Guohao. Predictive Model of Total Phosphorus Concentration in IPSO-SVR Waters Combined with Spectral Downscaling[J]. Bulletin of Soiland Water Conservation, 2024, 44(2): 196-204.
王彩玲, 张国浩. 结合光谱降维的IPSO-SVR水体总磷浓度预测模型[J]. 水土保持通报, 2024,44(2):196-204. DOI： 10.13961/j.cnki.stbctb.2024.02.021.

Wang Cailing, Zhang Guohao. Predictive Model of Total Phosphorus Concentration in IPSO-SVR Waters Combined with Spectral Downscaling[J]. Bulletin of Soiland Water Conservation, 2024, 44(2): 196-204. DOI： 10.13961/j.cnki.stbctb.2024.02.021.

摘要

[目的

]

选择最优模型对水体中总磷浓度进行预测，为准确、实时、高效检测水资源状况提供支持。 [方法

]

以2021年在长江中下游武汉—安徽地区采集的水质样本作为研究对象，首先，对采集到的长江光谱数据进行最大最小归一化和均值中心化两种预处理操作以便统一数据的范围和均值点，并使用核主成分分析(KPCA)技术对预处理后的光谱数据进行降维操作。选取方差解释率为99.6%下的6个特征向量进行后续预测模型的训练，接着在原有粒子群算法的基础上引入自适应惯性权重更新公式和遗传—模拟退火变异思想，提高算法的寻优能力。使用改进的粒子群优化算法对支持向量回归模型中的超参数组合进行寻优，对支持向量回归模型使用输出的结果进行预测模型的训练，最后使用测试集数据进行总磷浓度的预测。 [结果

]

提出了一种结合光谱降维的改进粒子群优化算法(IPSO)结合支持向量回归(SVR)的水体总磷含量预测模型。通过和当前预测性能较好的几种机器学习模型进行精度的比较发现，该试验模型对长江水体总磷浓度进行预测时决定系数(R

)为0.973 920，均方根差(RMSE)为0.003 012，平均绝对误差(MAE)为0.002 105。 [结论

]

使用光谱数据结合降维技术、粒子群优化算法和机器学习模型的算法融合模型检测水体总磷浓度可行性强，精确度高，且拟合效果良好。

Abstract

[Objective] The optimal model for predicting total phosphorus concentration in water bodies was studied in order to provide support for accurate

real-time

and efficient monitoring of water resources. [Methods] Water quality samples were collected in 2021 from the Wuhan-Anhui region in the middle and lower reaches of the Yangtze River. Firstly

the collected spectral data of the Yangtze River was preprocessed by both maximum-minimum normalization and mean centering to unify the range and mean point of the data. Kernel principal component analysis (KPCA) was then used to perform dimensionality reduction on the preprocessed spectral data. Six feature vectors were selected based on a variance explanation rate of 99.6% for training the subsequent prediction model. Next

an improved particle swarm optimization (IPSO) algorithm was proposed by introducing an adaptive inertia weight updating formula and a genetic-simulated annealing mutation concept to enhance the optimization ability of the algorithm. The improved particle swarm optimization algorithm was used to optimize the hyperparameter combinations in the support vector regression (SVR) model. The support vector regression model was trained using the output results to predict the total phosphorus concentration. Finally

the test set data were used to predict total phosphorus concentration. [Results] A prediction model for total phosphorus content in water using an improved particle swarm optimization (IPSO) combined with support vector regression (SVR) and spectral dimensionality reduction was proposed. The experimental model achieved an R2 of 0.973920

a root mean square error of 0.003012

and a mean absolute error of 0.002105 when predicting total phosphorus concentrations in the Yangtze River water. [Conclusion] The proposed method of using spectral data combined with dimensionality reduction techniques

particle swarm optimization algorithms

and machine learning models was determined to be feasible and effective in total phosphorus concentration measurement. The accuracy and fitting effects of the model were better than the accuracy obtained with several well-performing machine learning models.

关键词

Keywords

references

钟婷.4-氨基硫代酚调控的Au@Ag纳米颗粒的制备及其对亚硝酸盐的检测应用[D].四川成都:成都大学,2023. Zhong Ting. Preparation of Au@Ag nanoparticles regulated by 4-aminothiophenol and its application to the detection of nitrite[D]. Chengdu, Sichuan:Chengdu University, 2023.

薛同来,赵冬晖,韩菲.基于GA优化的SVR水质预测模型研究[J].环境工程,2020,38(3):123-127. Xue Tonglai, Zhao Donghui, Han Fei. SVR water quality prediction model based on GA optimization[J]. Environmental Engineering, 2020,38(3):123-127.

李建文,孟连子,刘娜,等.基于EEMD-SVR的渔业养殖水质预测模型[J].天津理工大学学报,2018,34(5):14-19,24. Li Jianwen, Meng Lianzi, Liu Na, et al. The prediction model of fishery water quality based on EEMD-SVR[J]. Journal of Tianjin University of Technology, 2018,34(5):14-19,24.

康贝,马洁.基于LLE-SVR的水质COD紫外光谱检测方法研究[J].传感器世界,2018,24(9):11-15. Kang Bei, Ma Jie. Study of UV visible spectrum-based COD detection method for water quality monitoring based on LLE-SVR[J]. Sensor World, 2018,24(9):11-15.

康成龙.基于SERS技术和GWO-SVR算法对水中污染物的检测研究[D].河北秦皇岛:燕山大学,2021. Kang Chenglong. Study on determination of pollutants in water based on SERS Technology and GWO-SVR algorithm[D]. Qinhuangdao, Hebei:Yanshan University, 2021.

姜吉光,石磊,苏成志,等.基于SPA-SVR的紫外光谱水质污染物含量解耦预测方法[J].激光与光电子学进展,2023,60(7):396-403. Jiang Jiguang, Shi Lei, Su Chengzhi, et al. Decoupled prediction method for water pollutant concentration based on SPA-SVR using ultraviolet spectroscopy[J]. Laser&Optoelectronics Progress, 2023,60(7):396-403.

刘豫,王顺钰,赵全顺.基于改进PSO-SVR的气温预测模型研究[J].青海科技,2022,29(5):148-153. Liu Yu, Wang Shunyu, Zhao Quanshun. Research on temperature prediction model based on improved PSO-SVR[J]. Qinghai Science and Technology, 2022,29(5):148-153.

刘合兵,王垒,王一飞,等.基于PSO-SVR模型的河南省小麦产量预测方法[J].江苏农业科学,2023,51(8):157-163. Liu Hebing, Wang Lei, Wang Yifei, et al. Wheat yield prediction method for Henan Province based on PSO-SVR model[J]. Jiangsu Agricultural Sciences, 2023,51(8):157-163.

朱成杰,汪正权.基于SPA-GA-SVR模型的土壤水分及温度预测[J].中国农村水利水电,2024(1):30-36. Zhu Chengjie, Wang Zhengquan. Soil moisture content and temperature prediction based on SPA-GA-SVR model[J]. China Rural Water and Hydropower, 2024(1):30-36.

任远芳,牛坤,丁静等.基于改进PSO算法优化SVR的信息安全风险评估研究[J].贵州大学学报(自然科学版),2024,41(1):103-109. Ren Yuanfang, Niu Kun, Ding Jing, et al. Research on risk assessment of information security based on SVR optimized by improved PSO algorithm[J]. Journal of Guizhou University (Natural Sciences), 2024,41(1):103-109.

杨俊祺,范晓军,赵跃华等.基于PSO-BP神经网络的山西省碳排放预测[J].环境工程技术学报,2023,13(6):2016-2024. Yang Junqi, Fan Xiaojun, Zhao Yuehua, et al. Prediction of carbon emissions in Shanxi Province based on PSO-BP neural network[J]. Journal of Environmental Engineering Technology, 2023,13(6):2016-2024.

王寿元,李积元,郎永存,等.基于PSO优化SVM数控机床主轴系统故障诊断的研究[J].组合机床与自动化加工技术,2023(9):151-155,159. Wang Shouyuan, Li Jiyuan, Lang Yongcun, et al. Research on fault diagnosis of CNC machine tool spindle system based on SVM Optimized by PSO[J]. Modular Machine Tool&Automatic Manufacturing Technique, 2023,9:151-155,159.

石延辉,杨洋,廖毅,等.基于改进粒子群算法优化SVM的变压器故障诊断[J].武汉大学学报(工学版),2023,56(10):1238-1244. Shi Yanhui, Yang Yang, Liao Yi, et al. Transformer fault diagnosis based on improved particle swarm optimization SVM[J]. Engineering Journal of Wuhan University, 2023,56(10):1238-1244.

龚泽威一,饶桐,王钢,等.基于改进粒子群优化XGBoost的变压器故障诊断方法[J].高压电器,2023,59(8):61-69. Gong Zeweiyi, Rao Tong, Wang Gang, et al. Fault diagnosis method of transformer based on improved particle swarm optimization XGBoost[J]. High Voltage Apparatus, 2023,59(8):61-69.

刘洋,张鸿,徐娟,等.改进PSO的SVM回归模型及在气温预测中的应用[J].计算机系统应用,2023,32(9):203-210. Liu Yang, Zhang Hong, Xu Juan, et al. Improved PSO SVM regression model and its application in temperature prediction[J]. Computer Systems&Applications, 2023,32(9):203-210.

赵海越,曹岩,黄亮,等.基于遗传算法的ZL205A铝合金切削参数多目标优化[J].兵器材料科学与工程,2023,46(5):58-65. Zhao Haiyue, Cao Yan, Huang Liang, et al. Multi-objective optimisation of cutting parameters of ZL205 A aluminium alloy based on genetic algorithm[J]. Ordnance Material Science and Engineering, 2023,46(5):58-65.

李艳波,李若尘,史博,等.基于改进模拟退火遗传算法的高速公路服务区自洽能源系统高能效优化[J].西安交通大学学报,2024,58(1):197-207,216. Li Yanbo, Li Ruochen, Shi Bo, et al. Optimization of high energy efficiency for self-consistent energy system in highway service area via simulated annealing algorithm-genetic algorithm[J]. Journal of Xi'an Jiaotong University, 2024,58(1):197-207.

王殿熙,杨波,成保忠,等.基于精英回溯-模拟退火混合算法的参数耦合变更路径优选[J].机械工程学报,1-18.(2023-09-22)[2024-05-12].http://kns.cnki.net/kcms/detail/11.2187.TH.20230922.1511.050.html. Wang Dianxi, Yang Bo, Cheng Baozhong, et al. Optimization of parameter coupling change propagation path based on elite backtracking-simulated annealing hybrid algorithm[J]. Journal of Mechanical Engineering, 1-18.(2023-09-22)[2024-05-12].http://kns.cnki.net/kcms/detail/11.2187.TH.20230922.1511.050.html.

孙嘉豪,张伟,施鉴芩,等.光谱数据预处理策略选择及应用[J].计量学报,2023,44(8):1284-1292. Sun Jiahao, Zhang Wei, Shi Jianqin, et al. Selection and application of spectral data preprocessing strategy[J]. Acta Metrologica Sinica, 2023,44(8):1284-1292.

阚相成,李耀翔,王立海,等.基于光谱预处理的低温水曲柳原木含水率检测[J].中南林业科技大学学报,2022,42(11):154-163. Kan Xiangcheng, Li Yaoxiang, Wang Lihai, et al. Moisture content detection of Fraxinus mandshurica logs at low temperatures based on different spectrum pretreatments[J]. Journal of Central South University of Forestry&Technology, 2022,42(11):154-163.

陈晓红,王辉,李喜华.基于KPCA-CNN-DBiGRU模型的短期负荷预测方法[J].管理工程学报,2024(2):221-231. Chen Xiaohong, Wang Hui, Li Xihua. A short-term load forecasting method based on KPCA-CNN-DBiGRU model[J]. Journal of Industrial Engineering and Engineering Management, 2024(2):221-231.

葛盼猛,陈波,刘庭赫,等.基于KPCA-ICFSFDP-MOGP的拱坝多测点变形数据预处理及预测方法[J].水利水电科技进展,2023,43(4):92-97. Ge Panmeng, Chen Bo, Liu Tinghe, et al. Preprocessing and prediction method for multi-point deformation data of arch dams based on KPCA-ICFSFDP-MOGP[J]. Advances in Science and Technology of Water Resources, 2023,43(4):92-97.

谢金燕,刘丽星,杨欣,等.改进粒子群优化算法的果园割草机作业路径规划[J].中国农业大学学报,2023,28(11):182-191. Xie Jinyan, Liu Lixing, Yang Xin, et al. Orchard lawn mower operation path planning based on improved particle swarm optimization algorithm[J]. Journal of China Agricultural University, 2023,28(11):182-191.

刘孝保,严清秀,易斌,等.基于集成学习和改进粒子群优化算法的流程制造工艺参数优化[J].中国机械工程,2023,34(23):2842-2853. Liu Xiaobao, Yan Qingxiu, Yi Bin, et al. Optimization of process parameters in process manufacturing based on ensemble learning and improved particle swarm optimization algorithm[J]. China Mechanical Engineering, 2023,34(23):2842-2853.

邵宁宁,王英.基于Adam优化的改进PSO-RBF牵引变压器故障诊断研究[J].电气工程学报,2023,18(4):209-216. Shao Ningning, Wang Ying. Research on PSO-RBF traction transformer fault diagnosis based on Adam optimization[J]. Journal of Electrical Engineering, 2023,18(4):209-216.

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