Effect of hydrodynamic conditions on the water quality in urban landscape water

Kang, Mengxin; Tian, Yuan; Hai-ya, Zhang; Wan, Che'ng

doi:10.2166/ws.2021.269

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…For the HD module, most studies show MAPE of less than 30%, indicating better simulation results [13,14].…”

Section: Model Verificationmentioning

confidence: 99%

Spatio-Temporal Simulation Analysis on Water Environment of Typical Eutrophic Tributaries of Three Gorges Reservoir Based on MIKE 21

Nan,

Chen,

Guan

et al. 2023

Advances in Transdisciplinary Engineering

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In recent years, the eutrophication in the typical tributaries of the Three Gorges Reservoir has attracted widespread attention. In the present paper, three typical eutrophic tributaries in the Three Gorges Reservoir area, that is, Xiaojiang River (Pengxi River), Daning River and Xiangxi River, were selected for research, and a two-dimensional hydrodynamic and water quality coupling model that is for the area from Zhutuo to dam site was constructed by using MIKE 21 software. On this basis, the temporal and spatial distribution of pollutants in three tributaries in 2021 was further simulated and analyzed. The results showed that: (1) The coupling model after calibration and validation had good simulation accuracy for hydrodynamic and water quality indexes in the reservoir area, and the simulation effect was in good agreement with the measured results; (2) The concentration of each pollutant was closely related to the change of water level and water quantity. From the highstand period to the pre-discharge stage (January-June), the concentrations of total nitrogen and total phosphorus increased, which were very high from the post-discharge period to the lowstand period (July-September); (3) The concentration of pollutants in the confluence of the main stream and tributaries was high due to the backward flow of the main stream. To sum up, the higher concentration of pollutants and the higher water temperature in spring and summer were conducive to the rapid reproduction of algae and other plankton, resulting in eutrophication, and the algal bloom was easy to break out in the reservoir area. During the water storage period and highstand period (October-December), the concentrations of total nitrogen and total phosphorus showed a downward trend, mainly due to the self-purification of water and the degradation of pollutants. The research conclusion can provide a reference for water quality evaluation, water supply safety and pollutant treatment in the Three Gorges Reservoir area.

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“…For the HD module, most studies show MAPE of less than 30%, indicating better simulation results [13,14].…”

Section: Model Verificationmentioning

confidence: 99%

Spatio-Temporal Simulation Analysis on Water Environment of Typical Eutrophic Tributaries of Three Gorges Reservoir Based on MIKE 21

Nan,

Chen,

Guan

et al. 2023

Advances in Transdisciplinary Engineering

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“…Numerical models, which derive results from rules and data [25], have been a frequently employed method for predicting changes in water quality [8,[26][27][28] and hydro-dynamic movement [29], with a variety of models available. Despite their capability to generate accurate simulations, their usage is significantly constrained by limitations.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Yan,

Zhang,

et al. 2024

JMSE

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Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction.

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“…Many researchers have used comprehensive water quality models to simulate and predict water quality [5][6][7][8][9]. At present, the comprehensive water quality models that have been widely used in water quality simulation and water environment management include the Water Quality Analysis Simulation Program (WASP) [10], QUAL series model [11],…”

Section: Introductionmentioning

confidence: 99%

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

Luo

Zhang

Dong

et al. 2023

Water

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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.

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Effect of hydrodynamic conditions on the water quality in urban landscape water

Cited by 6 publications

References 21 publications

Spatio-Temporal Simulation Analysis on Water Environment of Typical Eutrophic Tributaries of Three Gorges Reservoir Based on MIKE 21

Spatio-Temporal Simulation Analysis on Water Environment of Typical Eutrophic Tributaries of Three Gorges Reservoir Based on MIKE 21

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

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

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