结合光谱降维的IPSO-SVR水体总磷浓度预测模型

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中文摘要：[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(R2)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.

外文标题：Predictive Model of Total Phosphorus Concentration in IPSO-SVR Waters Combined with Spectral Downscaling

外文摘要：[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 0.973 920,a root mean square error of 0.003 012,and a mean absolute error of 0.002 105 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.

外文关键词：

hyperspectralIPSO-SVR modelingKPCA downscalingYangtze River water qualitytotal phosphorus concentration measurement

作者：

王彩玲、张国浩

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作者单位：

西安石油大学计算机学院,陕西西安 710065

关键词：

高光谱 IPSO-SVR模型 KPCA降维长江水质总磷浓度检测

基金：

陕西省重点研发计划国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金

项目编号：

2023-YBSF-4376140143941301382311604756200228662276213

出版年：

2024

DOI：

10.13961/j.cnki.stbctb.2024.02.021

水土保持通报

中国科学院水利部水土保持研究所水利部水土保持监测中心

水土保持通报

CSTPCD北大核心

影响因子：0.658

ISSN：1000-288X

年,卷(期)：2024.44(2)

参考文献量21