College of Life Science and Technology, Jinan University, 510632 Guangzhou, China.
College of Life Science and Technology, Jinan University, 510632 Guangzhou, China; Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, 510632 Guangzhou, China.
Sci Total Environ. 2021 Dec 10;799:149509. doi: 10.1016/j.scitotenv.2021.149509. Epub 2021 Aug 5.
Accurate and effective prediction of polycyclic aromatic hydrocarbons (PAHs) in surface water remains a substantial challenge due to the limited understanding of the dynamic processes. To assist integrated surface water management, a novel hybrid surface water PAH prediction model based on a two-stage decomposition approach and deep learning algorithm was proposed. Specifically, a two-stage decomposition technique consisting of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) was first introduced to decompose the data into several subsequences to extract the main fluctuations and trends of the PAH sequence. Subsequently, the deep learning algorithm long short-term memory (LSTM) was employed to explore the latent dynamic characteristics of each subsequence. Finally, the predicted values of the subsequences were integrated to obtain the final predicted results. An empirical study was conducted based on PAH data of eight major rivers in Saxony, Germany. The empirical results proved that the CEEMDAN-VMD-LSTM model outperformed other benchmark data-driven methods in predicting PAHs in surface water because it combined the advantages of two-stage decomposition and deep learning methods. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R) of the model were 27.89, 37.92 and 0.85, respectively. The proposed hybrid method can achieve effective and accurate water quality prediction and is an effective tool for surface water management.
由于对动态过程的了解有限，准确有效地预测地表水中的多环芳烃（PAHs）仍然是一个巨大的挑战。为了协助综合地表水管理，提出了一种基于两阶段分解方法和深度学习算法的新型混合地表水 PAH 预测模型。具体来说，首先引入了一个由完全集成经验模态分解与自适应噪声（CEEMDAN）和变分模态分解（VMD）组成的两阶段分解技术，将数据分解为几个子序列，以提取 PAH 序列的主要波动和趋势。然后，采用深度学习算法长短期记忆（LSTM）来探索每个子序列的潜在动态特征。最后，整合子序列的预测值以获得最终的预测结果。基于德国萨克森州的 8 条主要河流的 PAH 数据进行了实证研究。实证结果证明，CEEMDAN-VMD-LSTM 模型在预测地表水中的 PAHs 方面优于其他基准数据驱动方法，因为它结合了两阶段分解和深度学习方法的优势。该模型的平均绝对误差（MAE）、均方根误差（RMSE）和决定系数（R）分别为 27.89、37.92 和 0.85。所提出的混合方法可以实现有效的水质预测，是地表水管理的有效工具。
