Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach

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-2) 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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

A novel hybrid surface water PAH prediction model combining two-stage decomposition and deep learning algorithm was proposed for accurate and effective prediction of PAHs in surface water. Empirical results from eight major rivers in Saxony, Germany showed that the model outperformed other benchmark data-driven methods, with good prediction performance.