Accurate prediction of water quality in urban drainage network with integrated EMD-LSTM model

Quickly and accurately grasping the water quality in the drainage network is essential for the management and early warning of the urban water environment. Modeling-based detection methods enable fast and reagent-free water quality detection based on inexpensive multi-source data, which is cleaner and more sustainable than traditional chemical-reaction-based detection methods. But the unsatisfactory accuracy limits their practical application. This study proposes an integrated EMD-LSTM model that combines the data preprocessing module centered on empirical mode decomposition (EMD) and the long short-term memory (LSTM) neural network prediction module to improve the accuracy of the modeling-based detection methods. In the integrated EMDLSTM model, EMD allows retaining outliers and utilizing data on non-aligned moments, which contributes to capturing data patterns, while powerful nonlinear mapping and learning ability of LSTM neural network enables the time series prediction of water quality. As a result, the EMD-LSTM has achieved the highest R2 values (0.961, 0.9384, 0.9575, 0.9441, 0.9502) and the lowest RMSE values (8.3112, 6.7795, 0.2691, 2.6239, 1.4894) in the prediction of COD, BOD5, TP, TN, NH3-N when compared with the integrated models formed by combining other preprocessing procedures (i.e., traditional operation, short-time Fourier transform) and data-driven forecasting algorithms (i.e., partial least squares regression, gradient boosting regression, deep neural network). This study provides enlightenment for improving the accuracy of modeling-based detection methods, which has driven the development of water quality detection technology towards cleaner and more sustainable.

Automated summary

This study proposes an integrated EMD-LSTM model that combines data preprocessing and neural network prediction modules to improve the accuracy of modeling-based detection methods. The model achieved high accuracy in water quality prediction and provides insights for improving water quality detection technology.