基于PCA透射光谱重构降噪的水体BOD含量模拟估算  

Simulated Estimation of BOD Content in Water Bodies Based on PCA Transmission Spectrum Reconstruction With Noise Reduction

作　　者：王一鸣 王彩玲[1] 王洪伟 WANG Yi-ming;WANG Cai-ling;WANG Hong-wei(College of Computer Science,Xi'an Shiyou University,Xi'an 710065,China;School of Artificial Intelligence,Optics and Electronics,Northwestern Polytechnical University,Xi'an 710072,China)

机构地区：[1]西安石油大学计算机学院,陕西西安710065 [2]西北工业大学光电与智能研究院,陕西西安710072

出　　处：《光谱学与光谱分析》2025年第2期386-393,共8页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61401439,31160475);陕西省重点研发计划项目(2023-YBSF-437);西安石油大学研究生创新基金项目(YCX2413135)资助。

摘　　要：生化需氧量(BOD)是能够直接体现水体有机物污染程度的重要指标,水体BOD的实时监测在水资源保护、水环境改善等相关领域具有重要意义。传统的BOD测量方法会消耗大量的人力物力资源,且测量周期较长,不能迅速的反映水体的变化状况,无法实现对突发水污染事件及时有效的预警。机器学习在水体监测领域已被广泛应用,为了解决机器学习模型输入变量获取困难,且存在缺失值的问题,进一步结合高光谱技术探索对水体BOD含量精准快速的估算。为此,采集十个不同浓度BOD标液的原始光谱数据,通过白板校正得到100组透射光谱数据。提出了一种基于主成分分析(PCA)透射光谱重构的降噪技术,利用PCA算法提取原始透射光谱的主成分特征向量,再利用累计方差贡献率达到一定百分比的前一部分主成分特征向量对整个数据集进行重构。采用了前2、前10和前15个主成分特征向量对透射光谱数据进行了重构,并与传统光谱数据降噪方法进行了对比。结合支持向量机(SVM)模型和反向传播神经网络(BPNN)模型建立了水体BOD含量估算模型。结果显示,BPNN模型在回归精度和拟合程度上优于SVM模型,且降噪效果更为显著。使用前2个特征向量重构降噪的模型未达预期拟合,可能是由于信息丢失。而以前10个特征向量重构降噪的BPNN模型表现最佳,RMSE为0.0406,R^(2)达到0.9803。前15个特征向量的重构并未提升降噪效果,可能因为超过10个的特征向量增加了冗余信息。实验验证了使用PCA重构透射光谱降噪的可行性,并为水体BOD含量估算提供了新的思路。Biochemical oxygen demand(BOD)is an important indicator that can directly reflect water bodies'degree of organic pollution.Real-time monitoring of water BOD is significant in water resource protection and water environment improvement.The traditional BOD measurement method will consume a lot of human and material resources,and the measurement cycle is long,which can not quickly reflect the changing conditions of the water body,and can not realize the timely and effective early warning of sudden water pollution events.With the wide application of machine learning in the field of water monitoring,to solve the problem of difficulty in obtaining the input variables of the machine learning model and the existence of missing values,we further combine the hyperspectral technology to realize the accurate and rapid estimation of the BOD content of the water body.The raw spectral data of ten BOD standard liquids with different concentrations were collected,and 100 sets of transmission spectral data were obtained by whiteboard correction.A noise reduction technique based on PCA transmission spectra reconstruction is proposed,which utilizes the PCA algorithm to extract the principal component eigenvectors of the original transmission spectra and then reconstructs the whole dataset by using the first part of the principal component eigenvectors whose cumulative variance contribution rate reaches a certain percentage.The first 2,10,and 15 principal component feature vectors were used in the experiment to reconstruct the transmission spectral data and compared with the traditional noise reduction methods for spectral data.We combined the SVM model and BP neural network model to establish a model for estimating the BOD content of water bodies.The results showed that the BPNN model was superior to the SVM model regarding regression accuracy and degree of fit,and the noise reduction effect was more significant.The model using the first 2 feature vectors reconstructed for noise reduction did not fit as expected,probably due to the

关 键 词：PCA 透射光谱 SVM BP神经网络 BOD含量估算 

分 类 号：O657.3[理学—分析化学]