Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model

Barzegar, Rahim; Aalami, Mohammad Taghi; Adamowski, Jan

doi:10.1007/s00477-020-01776-2

Cited by 313 publications

(142 citation statements)

References 76 publications

Supporting

Mentioning

107

Contrasting

Order By: Relevance

“…Increasing the forecasting steps of each round of prediction may help to reduce computational consumption, while relevant previous studies rarely discussed the impact of the increase in forecasting steps on model performances [14,15]. In this study, all of the models were tested for one-step-ahead (4 h) to six-step-ahead (1 day) forecasting, which were aimed to analyze the impact of the forecasting steps on the performances of the models.…”

Section: Discussionmentioning

confidence: 99%

“…Among the multiple DL models, long short-term memory (LSTM) and convolutional neural networks (CNN) are the two most commonly used models. Barzegar et al used standalone LSTM and CNN and hybrid CNN-LSTM models to predict DO and Chl-a in the Small Prespa Lake in Greece, and found that the hybrid model was superior to the standalone models [14]. Hao et al applied singular spectrum analysis and ensemble empirical mode decomposition to preprocess the data at first, and found that the performances of LSTM model were better than using the raw data directly [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Sha

Zhang

et al. 2021

Water

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Sha

Zhang

et al. 2021

Water

View full text Add to dashboard Cite

show abstract

“…The combined CNN-LSTM model, however, outperformed the standalone models for predicting both DO and Chl-a. By coupling the LSTM and CNN models, both the low and high levels of water quality parameters were successfully captured, particularly for the DO concentrations (Barzegar et al, 2020). Similar successful approaches involving the coupling of multiple ML algorithms for the short-term prediction of water quality parameters include Li et al (2018) and Lu and Ma (2020).…”

Section: Water Qualitymentioning

confidence: 95%

“…They found that while the hybrid XGBoost model performed better for PH values, turbidity, and fluorescent dissolved organic matter predictions, and the random forest model performed better for temperature, dissolved oxygen, and specific conductance prediction; the combined performance of the two models was the best for optimizing the calculation of a water quality index. Barzegar et al (2020) applied two standalone deep learning (DL) models, a convolutional neural network (CNN), an ANN with a convolutional activation function, and the long short-term memory (LSTM) model, which includes feedback in addition to feedforward networks, and a combined CNN-LSTM model to predict two water quality variables, dissolved oxygen (DO; mg/L), and chlorophyll-a (Chla; µ/L), in the Small Prespa Lake in Greece. Assessment of the model performance using statistical metrics, showed that LSTM outperformed the CNN model for DO prediction, but the standalone DL models yielded similar performances for Chl-a prediction.…”

Section: Water Qualitymentioning

confidence: 99%

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Allen‐Dumas

Kurte

et al. 2021

Front. Water

View full text Add to dashboard Cite

Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach. However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways. In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan. In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas. We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.

show abstract

“…Long short-term memory (LSTM), a type of recurrent neural network (RNN), has been widely used as an efficient tool to simulate and predict water quality due to an ability to extract features from time-series data ( lin Hsu et al, 1997 ). For example, Barzegar et al (2020) recently utilized L STM and L STM hybrid models to predict water quality variables in a lake. An advantage of LSTM is that it can use memory to learn features over time.…”

Section: Introductionmentioning

confidence: 99%

Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models

et al. 2021

View full text Add to dashboard Cite

Antibiotic resistance genes (ARGs) have been reported to threaten the public health of beachgoers worldwide. Although ARG monitoring and beach guidelines are necessary, substantial efforts are required for ARG sampling and analysis. Accordingly, in this study, we predicted ARGs occurrence that are primarily found on the coast after rainfall using a conventional long short-term memory (LSTM), LSTMconvolutional neural network (CNN) hybrid model, and input attention (IA)-LSTM. To develop the models, 10 categories of environmental data collected at 30-min intervals and concentration data of 4 types of major ARGs (i.e., aac(6 -Ib-cr ), bla TEM , sul 1, and tet X) obtained at the Gwangalli Beach in South Korea, between 2018 and 2019 were used. When individually predicting ARGs occurrence, the conventional L STM and IA-L STM exhibited poor R 2 values during training and testing. In contrast, the LSTM-CNN exhibited a 2-6-times improvement in accuracy over those of the conventional L STM and IA-L STM. However, when predicting all ARGs occurrence simultaneously, the IA-LSTM model exhibited a superior performance overall compared to that of LSTM-CNN. Additionally, the influence of environmental variables on prediction was investigated using the IA-LSTM model, and the ranges of input variables that affect each ARG were identified. Consequently, this study demonstrated the possibility of predicting the occurrence and distribution of major ARGs at the beach based on various environmental variables, and the results are expected to contribute to management of ARG occurrence at a recreational beach.

show abstract

Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model

Cited by 313 publications

References 76 publications

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

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

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models

Contact Info

Product

Resources

About