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中国工业与应用数学学会会刊
主管：中华人民共和国教育部
主办：西安交通大学
ISSN 1005-3085  CN 61-1269/O1
工程数学学报

工程数学学报

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基于CEEMDAN的人工蜂群算法优化LSTM深度网络的西江溶解氧预测

纪广月   

  1. 广东工商职业技术大学通识教育中心，肇庆 526020
  • 收稿日期:2020-03-11 接受日期:2020-06-18 出版日期:2021-06-15 发布日期:2021-08-15
  • 基金资助:
    广东省教育厅高校特色创新类项目 (自然科学) (2017GKTSCX109).

Dissolved Oxygen Prediction of Xijiang River with the LSTM Deep Network by Artificial Bee Colony Algorithm Based on CEEMDAN

JI Guang-yue   

  1. General Education Center, Guangdong University of Business and Technology, Zhaoqing 526020
  • Received:2020-03-11 Accepted:2020-06-18 Online:2021-06-15 Published:2021-08-15
  • Supported by:
    The Characteristic Innovation Project of Department of Education of Guangdong Province (Natural Science) (2017GKTSCX109).

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摘要： 为提高溶解氧含量预测的精度，提出一种基于添加自适应白噪声的完备集成经验模态分解法(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, CEEMDAN)和人工蜂群算法(Artificial Bee Colony, ABC)改进长短时记忆
网络(Long Short-term Memory, LSTM)的水质溶解氧组合预测模型．首先，运用CEEMDAN算法将溶解氧含量序列分成若干个不同固有模态分量和趋势分量；之后，运用R/S类分析法计算不同固有模态分量和趋势分量的Hurst指数H，并根据H的大小将不同固有模态分量和趋势分量重构成微尺度、中尺度和宏尺度分量；最后，针对三种尺度分量分别运用ABC-LSTM模型进行预测并线性加权重构获得溶解氧最终预测值．该模型以西江中山横栏水质监测站点数据采集系统为研究对象，试验结果表明，本文模型可以有效提高西江溶解氧预测精度，预测精度高达1.6978%，较LSTM、支持向量机(Support Vector Machine, SVM)、ABC-SVM和人工蜂群算法优化前馈神经网络(Back Propagation Neutral Network, BPNN)ABC-BPNN分别提高1.2867%、2.7544%、2.3756%和2.4448%，从而说明本文模型较传统模型精度上有明显提高，具有更强的预测性能和泛化能力、误差更低，为西江水质监测管理和维护提供科学决策的依据．

关键词: CEEMDAN原理, 经验模态分解, 人工蜂群算法, 长短时记忆网络, 溶解氧

Abstract: In order to improve the prediction accuracy of dissolved oxygen content, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed and the artificial bee colony (ABC) is used to improve the long and short time memory network (LSTM) model for combined prediction of dissolved oxygen in water quality. First, CEEMDAN is used to calculate the dissolved oxygen content sequence, which is divided into several different natural mode components and trend components. After that, we apply the R/S class to compute the Hurst exponent H of different natural mode components and trend components, and according to the value of H, natural mode components and trend components are re-constructed into microscale, mesoscale and macroscale components, respectively. Finally, for three kinds of components, the ABC-LSTM model is used to predict three species scale components, and the linear weighted reconstruction method is used to obtain the final estimates of dissolved oxygen measured values. The proposed model is applied to the data acquisition system of Henglan water quality monitoring station in Xijiang River. The results show that the model can effectively improve the prediction accuracy of dissolved oxygen in Xijiang River, and the prediction accuracy is as high as 1.6978%. Compared with LSTM, support vector machine (SVM), the ABC-SVM and the artificial bee colony algorithm optimized back propagation neutral network (ABC-BPNN), the prediction accuracy is improved by respectively 2.2867%, 2.7544%, 2.3756% and 2.4448%, indicating that the model in this paper has higher accuracy than the traditional models, with better prediction performance and generalization ability, as well as lower error. This provides the basis for scientific decision-making for the Xijiang water quality monitoring management and maintenance.

Key words: CEEMDAN principle, empirical mode decomposition, artificial bee colony algorithm, long short memory network, dissolved oxygen

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