改进粒子群优化算法结合BP神经网络模型的水体透射光谱总磷浓度预测研究

doi:10.3964/j.issn.1000-0593(2025)02-0394-09

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摘要：使用光谱数据结合融合算法对水体污染物含量进行准确检测以保护水资源已成为一个关键问题。然而，光谱数据的高维特性以及模型的不稳定常常导致预测效果不佳，无法准确的进行检测。本研究提出了一种环保和准确的方法，实现对长江水体中总磷浓度含量的预测。具体而言，首先对测得的长江水质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，在消除不同数据量级差异的同时去除了噪声，确保了数据的一致性和可靠性。其次，为了解决光谱数据的高维度问题，采用了核主成分分析（KPCA）方法来降低数据维度并提取特征。KPCA方法通过在高维度的空间中找到一个分类平面，选出能代表原始数据99.42%信息量的前6个主成分，用于后续预测模型的训练。接着在原始粒子群算法的基础上引入了粒子初始化规则、多种群竞争策略、参数自适应更新策略、种群多样性引导策略和粒子变异机制，提高了粒子群的寻优能力，降低粒子陷入局部最优解的概率。并使用改进后的粒子群算法对BP神经网络（BPNN）中的初始化权重和参数大小进行寻优，从而加快网络的收敛效果，提高预测能力。最后，使用本研究所提出的预测模型对测试集中的样本进行总磷浓度的预测，实验结果得到R²为0.975 786，RMSE为0.002 242，MAE为0.001 612。将本模型与当前预测性能较好的其他基准模型进行预测效果的对比，本研究所提出的模型对长江水体总磷浓度预测拟合效果更好，精确度更高。在水资源保护和环境管理领域中使用光谱数据结合融合算法进行预测模型的研究和实践提供了新的思路和观点。

关键词：光谱数据；改进粒子群优化算法；BP神经网络模型；核主成分分析(KPCA)；总磷浓度

Abstract：The accurate detection of pollution levels in water bodies using transmission spectrum data and fusion algorithms has become crucial for safeguarding water resources. Inaccurate predictions and detection frequently result from the high-dimension of transmission spectrum data and model instability. The Yangtze River water body's total phosphorus concentration content is predicted in this study, and an accurate and environmentally friendly approach is suggested to achieve this goal. In particular, maxi-min normalization and mean-centering are two preprocessing operations carried out on the Yangtze River's measured water quality transmission spectrum data. These operations remove noise while eradicating differences between different data magnitudes, guaranteeing the consistency and reliability of the data. In addition, to solve the problem of the high dimension of the transmission spectrum data, the KPCA method is used to reduce the dimension of the data and extract the features. The KPCA method is used to select the top 6 principal components that represent 99.42% of the information content of the original data for subsequent prediction model training by finding a classification plane in a high-dimension space. Then, on the foundation of the initial particle swarm algorithm, the particle initialization rule, multiple swarm competition strategy, parameter adaptive update strategy, population diversity guidance strategy, and particle variation mechanism are added to improve the particle swarm's capacity for optimization and prevent particles from trapping in the local optimal solution. Additionally, the improved particle swarm algorithm optimizes the initialized weights and parameter values in the BP neural network to accelerate the convergence of the network and improve prediction performances. Finally, the total phosphorus content of the samples in the test set was predicted using the IMCPSO-BPNN model. The experimental results showed an R² of 0.975 786, an RMSE of 0.002 242, and an MAE of 0.001 612. The IMCPSO-BPNN model suggested in this work has a better fitting effect and better accuracy in forecasting the total nitrogen concentration in the Yangtze River water body when compared to other models such as the RF model, the BPNN model, and the PSO-BPNN model. It offers fresh concepts and viewpoints for studying and applying predictive modeling using transmission spectrum data and fusion algorithms to protect water resources and environmental management.

Key words：Transmission spectrum; Improved particle swarm Optimization algorithm; BP Neural network; Kernel principal component analysis (KPCA); Total phosphorus concentration

收稿日期: 2023-10-03 修订日期: 2024-06-17

中图分类号:

O657.3

基金资助: 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），中国科学院战略性先导科技专项（A类）（XDA23040101），西安石油大学研究生创新与实践能力培养项目（YCS23213173），西安石油大学研究生创新基金项目(YCX2412041)资助

通讯作者: 王彩玲，王洪伟 E-mail: azering@163.com；wanghongwei@nwpu.edu.cn

作者简介: 张国浩，2000年生，西安石油大学计算机学院硕士研究生 e-mail: zhangguohaogh@163.com

引用本文:

张国浩，王彩玲，王洪伟，于涛. 改进粒子群优化算法结合BP神经网络模型的水体透射光谱总磷浓度预测研究[J]. 光谱学与光谱分析, 2025, 45(02): 394-402.
ZHANG Guo-hao, WANG Cai-ling, WANG Hong-wei, YU Tao. Improved Particle Swarm Optimization Algorithm Combined With BP Neural Network Model for Prediction of Total Phosphorus Concentration in Water Body Using Transmittance Spectral Data. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(02): 394-402.

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