Ensemble Empirical Mode Decomposition and a Long Short-Term Memory Neural Network for Surface Water Quality Prediction of the Xiaofu River, China

Abstract

Water quality prediction is an important part of water pollution prevention and control. Using a long short-term memory (LSTM) neural network to predict water quality can solve the problem that comprehensive water quality models are too complex and difficult to apply. However, as water quality time series are generally multiperiod hybrid time series, which have strongly nonlinear and nonstationary characteristics, the prediction accuracy of LSTM for water quality is not high. The ensemble empirical mode decomposition (EEMD) method can decompose the multiperiod hybrid water quality time series into several simpler single-period components. To improve the accuracy of surface water quality prediction, a water quality prediction model based on EEMD–LSTM was developed in this paper. The water quality time series was first decomposed into several intrinsic mode function components and one residual item, and then these components were used as the input of LSTM to predict water quality. The model was trained and validated using four water quality parameters (NH3-N, pH, DO, CODMn) collected from the Xiaofu River and compared with the results of a single LSTM. During the validation period, the R2 values when using LSTM for NH3-N, pH, DO and CODMn were 0.567, 0.657, 0.817 and 0.693, respectively, and the R2 values when using EEMD–LSTM for NH3-N, pH, DO and CODMn were 0.924, 0.965, 0.961 and 0.936, respectively. The results show that the developed model outperforms the single LSTM model in various evaluation indicators and greatly improves the model performance in terms of the hysteresis problem. The EEMD–LSTM model has high prediction accuracy and strong generalization ability, and further development may be valuable.

Improved Deep Learning Predictions for Chlorophyll Fluorescence Based on Decomposition Algorithms: The Importance of Data Preprocessing

“…In this study, the Daubechies-4 (db4) mother wavelet was utilized to perform a three-level decomposition of the original time series, a choice popular for its widespread acceptance and efficient performance [26]. The ensemble number of the EEMD model was set to 100, and the standard deviation of Gaussian white noise, n i (t), was 0.05 [30]. We set the window length of the SSA to 15 according to the empirical evaluation of component contributions in the experiment.…”

“…This is in line with the previous study by Liu et al [26], who demonstrated that the decomposition-based hybrid WT-LSTM model could enhance the fitting of abrupt or extreme data points and improve HAB prediction performance. Likewise, Luo et al [30] developed a hybrid EEMD-LSTM model for predicting water quality, and the results demonstrated that the proposed model outperformed the individual LSTM model in various evaluation indicators. Cui et al [42] also discovered that integrating SSA with a lightweight gradient-boosting machine in a hybrid model led to high-accuracy, real-time predictions of urban runoff.…”

“…The noise-assisted ensemble empirical mode decomposition (EEMD) method is utilized to address the issue of mode mixing and is capable of processing complex and non-stationary time series. EEMD has gained extensive adoption in enhancing the forecasting performance of precipitation [47], daily runoff [48], water levels [49], rainfall [50], streamflow [51], river flow [52], and water quality [30]. Singular spectral analysis (SSA) has found widespread applications in the preprocessing of hydrological data, including streamflow [31], rainfall [53], runoff [54], and rainfall runoff [42] prediction.…”

“…Precipitation [47] Daily Runoff [48] Water Level [49] Rainfall [50] Streamflow [51] River flow [52] Water Quality [30] Singular Spectrum Analysis (SSA) Strict mathematical theory. Decompose a time series into distinct components.…”

“…Zhu et al [29] reported the hybrid EMD-LSTM with the attention mechanism model and indicated that empirical mode decomposition (EMD) can effectively enhance the smoothness of the time series and increase predictive accuracy. In addition, Luo et al [30] develop an improved empirical mode decomposition model (EEMD)-LSTM prediction model to predict water quality. Apaydin et al [31] investigate the singular spectral analysis (SSA) with LSTM to increase the monthly streamflow prediction accuracy.…”