A combined catchment‐reservoir water quality model to guide catchment management for reservoir water quality control

Chen, Chi‐Feng; Chong, Kean‐Yip; Lin, Jen‐Yang

doi:10.1111/wej.12695

Cited by 5 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advantage of these methods is that their parameters are easy to tune and express, and they have a very clear physical meaning; also, the prediction model is robust and adaptable. Commonly used mechanism models generally include the MIKE model, CE-QUAL-W2 model, EFDC model, and WASP model [7][8][9][10]. However, due to the complex internal mechanism of some water bodies, it is difficult and time-consuming to directly describe the evolution process of water quality with mathematical expressions.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the existing water quality modeling or time-series predicting methods are based on the mechanism or the LSTM model [7][8][9][10]26]. This paper not only avoids the complexity of constructing a mechanistic model but also extracts data features to reduce the dependence on data quality, which proposes an improved deep belief network (DBN) and LSTM fusion method to construct a water quality prediction model based on a data-driven approach, and constructs a DBN with Gaussian Restricted Boltzmann Machines (GRBMs) stacking to improve the algorithm, noted as GDBN, which solves the problem of data loss in the feature extraction of the classical DBN neuron binary problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Water Quality Prediction and Assessment Method Based on the Improved Deep Belief Network—Long Short-Term Memory Model

Zhao,

Fan,

Zhou

2024

Water

View full text Add to dashboard Cite

The accuracy of water quality prediction and assessment has always been the focus of environmental departments. However, due to the high complexity of water systems, existing methods struggle to capture the future internal dynamic changes in water quality based on current data. In view of this, this paper proposes a data-driven approach to combine an improved deep belief network (DBN) and long short-term memory (LSTM) network model for water quality prediction and assessment, avoiding the complexity of constructing a model of the internal mechanism of water quality. Firstly, using Gaussian Restricted Boltzmann Machines (GRBMs) to construct a DBN, the model has a better ability to extract continuous data features compared to classical DBN. Secondly, the extracted time-series data features are input into the LSTM network to improve predicting accuracy. Finally, due to prediction errors, noise that randomly follows the Gaussian distribution is added to the assessment results based on the predicted values, and the probability of being at the current water quality level in the future is calculated through multiple evolutionary computations to complete the water quality assessment. Numerical experiments have shown that our proposed algorithm has a greater accuracy compared to classical algorithms in challenging scenarios.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Water Quality Prediction and Assessment Method Based on the Improved Deep Belief Network—Long Short-Term Memory Model

Zhao,

Fan,

Zhou

2024

Water

View full text Add to dashboard Cite

show abstract

“…Namely, mechanical models typically have a large number of parameters, and if these parameters are not well-tuned or there are large errors, the simulation effect of the models could be significantly affected. Commonly used mechanism models generally include the MIKE model, CE-QUAL-W2 model, EFDC model, and WASP model [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An efficent water quality prediction and evaluation method based on the DBN-LSTM model

Fan

Zhao

2023

Preprint

View full text Add to dashboard Cite

Currently, water environmental issues have been severe. Water quality not only affects the ecological balance of nature but is also closely related to human lives. Therefore, an accurate water quality prediction and assessment can prevent water eutrophication, thus protecting the environment and improving people's quality of life. Previous studies have shown that the Long Short-Term Memory (LSTM) neural network has a good performance in time series prediction. In view of that, this paper proposes a neural network model that integrates the Deep Belief Network (DBN) and LSTM network for water quality prediction. The real data are used to predict water quality, and the DBN network is used to extract data features, which are then fed to the LSTM network. The proposed combined model is verified by experiments, and experimental results show that the proposed DBN-LSTM model can significantly improve prediction accuracy compared to the LSTM. The results indicate that the addition of the DBN network can improve the feature extraction and denoising performances on various datasets. The proposed water quality prediction model is compared with both single- and multiple-factor evaluation methods. The comparison results show that the DBN-LSTM model outperforms the LSTM model in terms of prediction accuracy, where it can be used in water quality monitoring management and risk assessment.

show abstract

“…There are different methods and approaches to couple these two models. Various commonly used coupling models have been reported, i.e., the coupling of HEC and water quality analysis simulation program (WASP), hydrological simulation program-Fortran (HSPF) and SWAT, HSPF and WASP, SWAT and CE-QUAL-W2, , and SWAT and EFDC. , Despite the aforementioned reports, these coupling models were not summarized and compared in detail, specifically their simulation mechanisms and application fields.…”

Section: Introductionmentioning

confidence: 99%

Watershed Models: Review of Approaches, Challenges, and Opportunities

Jiang,

Shen

2023

ACS EST Water

View full text Add to dashboard Cite

Watershed modeling is a crucial tool needed for understanding, evaluating, and predicting the adverse impact of water pollution. The watershed model can be divided into an upland watershed model (UWSM) and a downstream waterbody model (DWBM). Neither a single UWSM nor DWBM are able to accurately simulate the pollutant transport along with the surface flow in complex upland watershed−waterbody systems. Therefore, coupled UWSM and DWBM are more desirable. This study categorized the coupled UWSM and DWBM into external and internal coupling models. The coupling mechanism, numerical accuracy, computational efficiency, and applications of these coupling models are discussed. A dynamic bidirectional coupled model for environment simulation (E-DBCM) is consistent with the natural flood and pollutant transport processes, which can improve computational efficiency while maintaining simulation accuracy. Compared with the original coupling models, it possesses three advantages: (1) reasonable coupling mechanisms, (2) complete function, and (3) high efficiency and simplicity. E-DBCM is a potential watershed water environment model, which can be widely used if improved further. The findings of this review will help accurately and efficiently predict and manage pollutants worldwide.

show abstract

A combined catchment‐reservoir water quality model to guide catchment management for reservoir water quality control

Cited by 5 publications

References 20 publications

An Efficient Water Quality Prediction and Assessment Method Based on the Improved Deep Belief Network—Long Short-Term Memory Model

An Efficient Water Quality Prediction and Assessment Method Based on the Improved Deep Belief Network—Long Short-Term Memory Model

An efficent water quality prediction and evaluation method based on the DBN-LSTM model

Watershed Models: Review of Approaches, Challenges, and Opportunities

Contact Info

Product

Resources

About