IPSO-BPNN:一种结合粒子群优化的BP神经网络透射光谱水质亚硝酸盐含量定量化模型  

IPSO-BPNN:A Quantitative Model for Nitrite Content in Water Quality Using Transmissive Spectroscopy Combined With Improved Particle Swarm Optimizat ion and Backpropagation Neural Network

作　　者：王彩玲[1] 张国浩 WANG Cai-ling;ZHANG Guo-hao(College of Computer Science,Xi'an Shiyou University,Xi'an 710065,China)

机构地区：[1]西安石油大学计算机学院,陕西西安710065

出　　处：《光谱学与光谱分析》2024年第11期3172-3178,共7页Spectroscopy and Spectral Analysis

基　　金：陕西省重点研发计划项目(2023-YBSF-437);国家自然科学基金项目(61401439,41301382,31160475,62002286,62276213);西安石油大学研究生创新与实践能力培养项目(YCS23213173)资助。

摘　　要：亚硝酸盐是一种常见的水质污染物,主要来源为废水、肥料和污水处理厂等。水质中亚硝酸盐浓度大小是评估水体健康程度的一个重要指标,但传统的亚硝酸盐浓度检测方法操作复杂且容易受到检测环境的干扰,无法直观和准确的反映出水质健康程度。为了探究一种新的方式来评估水体的健康程度,使用IPSO-BPNN模型对亚硝酸盐透射光谱数据进行浓度预测。首先选择10种浓度的亚硝酸盐标准溶液(0.02、0.04、0.06、0.08、0.10、0.12、0.14、0.16、0.18和0.20 mg·L^(-1),使用OCEAN-HDX-XR微型光谱仪在相同的时间间隔下对十个浓度的亚硝酸盐溶液进行扫描,并通过白板校正得到光谱数据的光谱透射率值。使用最大最小归一化、均值中心化两种预处理方法将光谱数据进行维度和中心点的统一,使得不同样本之间的光谱数据具有可比性和可解释性。由于原始光谱数据维度较高,采用核主成分分析进行数据降维,选择代表原始数据97.94%信息的6个主成分进行IPSO-BPNN模型的训练。在预测亚硝酸盐浓度时,对原始粒子群优化算法进行了改进,引入了自适应学习因子和惯性权重更新公式以及粒子种群多样性引导策略,并在BP神经网络的基础上引入了学习率自适应公式,提高了算法的性能。通过比较不同粒子数进行迭代的函数适应度值变化曲线,选择使用100个粒子进行30次迭代来寻找最优权重和偏置组合。结果显示,IPSO-BPNN预测模型的决定系数为0.984360,均方根误差为0.006920,平均绝对误差为0.004103,与当前预测性能较好的随机森林模型、线性回归模型、BP-ANN模型、PSO-BPNN模型和PSO-SVR模型相比,该模型的拟合效果更好,精确度更高。基于以上结果,提出了一种基于IPSO-BPNN模型的高光谱水质亚硝酸盐浓度预测方法,为水体健康程度的评估提供了新的思路。Nitrite is a common water quality pollutant and is the main source of wastewater,fertilizer,and sewage treatment plants.The size of nitrite concentration in water quality is an important indicator to assess the health of water bodies.Still,the traditional method of nitrite concentration detection is complicated to operate.It easily interferes with the detection environment,which can not intuitively and accurately reflect the health of water quality.To explore a new way to assess the health of water bodies,this paper uses the IPSO-BPNN model to predict the concentration of nitrite transmission spectral data.Ten concentrations of nitrite standard solutions(0.02,0.04,0.06,0.08,0.10,0.12,0.14,0.16,0.18,and 0.20 mg·L^(-1))are first selected,and the ten concentrations of nitrite solutions are scanned at the same time intervals by using the OCEAN-HDX-XR micro spectrometer,The spectral transmittance of the spectral data is obtained by white board calibration to obtain spectral transmittance values for the spectral data.Two preprocessing methods,maximum-minimum normalization,and mean-centering,are used to unify the spectral data into uniform dimensions and centroids,making the spectral data comparable and interpretable among different samples.Due to the high dimensionality of the original spectral data,kernel principal component analysis is used for data dimensionality reduction,and six principal components representing 97.94%of the original data information are selected for the training of the IPSO-BPNN model.When predicting nitrite concentration,the original particle swarm optimization algorithm is improved by introducing adaptive learning factor and inertia weight updating formulae and particle population diversity guiding strategy,and learning rate adaptive formulae are introduced based on the BP neural network to improve the algorithm's performance.By comparing the change curves of function fitness values for iterations performed under different particles,30 iterations using 100 particles are chosen to find the opti

关 键 词：高光谱 亚硝酸盐 IPSO-BPNN模型 KPCA 水质检测 

分 类 号：O657.3[理学—分析化学]