计算机科学
人工神经网络
希尔伯特-黄变换
非线性系统
算法
模式(计算机接口)
奇异谱分析
分解
噪音(视频)
人工智能
滤波器(信号处理)
奇异值分解
化学
物理
图像(数学)
操作系统
量子力学
计算机视觉
有机化学
作者
Yituo Zhang,Chaolin Li,Yiqi Jiang,Ruobin Zhao,Kefen Yan,Wenhui Wang
出处
期刊:Applied Energy
[Elsevier]
日期:2023-03-01
卷期号:333: 120600-120600
被引量:9
标识
DOI:10.1016/j.apenergy.2022.120600
摘要
Timely and accurately grasping total phosphorus (TP) concentration in sewer networks is crucial for urban phosphorus flow management and shock load early warning of sewage treatment facilities. Modeling-based methods are cleaner and more energy-saving than traditional methods requiring digestion procedures. However, the TP time series' strong nonlinearity and complexity result in unsatisfactory accuracy in these methods. This work proposes a hybrid model named CEEMDAN-SE-VMD-LSTM that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), variational mode decomposition (VMD), and long short-term memory (LSTM) neural network to improve the accuracies of modeling-based methods. In proposed hybrid model, the two-stage decomposition procedure can decompose the TP time series into several lower-complexity modes, and the powerful nonlinear mapping ability of the LSTM neural network enables accurate prediction of these modes. In case study, the proposed hybrid model achieves excellent detection accuracy with an average R2 of 0.9460 ± 0.0243. Compared with the hybrid models formed by combining other decomposition procedures (i.e., CEEMDAN, VMD, singular spectrum analysis (SSA), CEEMDAN-SE-SSA) and LSTM neural network, the proposed hybrid model has the highest detection accuracy (1.36–3.94 % higher Nash-Sutcliffe efficiency, 1.28–4.58 % higher R2, 12.14–24.86 % lower RMSE). The strategy of setting the mode decomposition procedure based on a comprehensive analysis of the decomposition algorithms and the obtained modes ensures high detection accuracy of the proposed hybrid model while avoiding costly computational burdens. This work is enlightening for improving the accuracy and modeling efficiency of soft detection methods, which are expected to reduce energy consumption and pollution caused by water quality detection.
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