Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Abstract

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

Recognizing Safe Drinking Water and Predicting Water Quality Index using Machine Learning Framework

“…Other hybrid machines and deep learning models have been developed for investigating water quality index, for example, one-dimensional residual CNN (1-DRCNN) and bi-directional gated recurrent units (BiGRU) have been utilized for predicting Water Quality in the Luan River [31]. Moreover, a hybrid deep learning model based on the CNN and LSTM model has been applied, tested, and compared for predicting water goodness based on real-time monitoring of water quality variables [32].…”

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

“…Sha et al [99] evaluated various DL approaches such as CNN, LSTM, and CNN-LSTM models. Moreover, they employed a complete ensemble empirical mode decomposition algorithm (EEMD) with adaptive noise (CEEMDAN) to decompose and reduce the intricacy of DO and TN concentration.…”

Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy

“…Since the initial stage of water quality forecasting research, nonmechanical water quality forecasting methods have emerged. The theoretical bases of non‐mechanical water quality forecasting methods include regression, time sequence analysis, gray forecasting, and neural networks (Sha et al, 2021). Khan and Valeo (2015) proposed a fuzzy linear regression model to forecast the dissolved oxygen content of a river in Calgary, Canada.…”