Optimizing the Water Treatment Design and Management of the Artificial Lake with Water Quality Modeling and Surrogate-Based Approach

Liu, Chuankun; Hu, Yue; Yu, Ting; Xu, Qiang; Liu, Chaoqing; Li, Xi; Shen, Chao

doi:10.3390/w11020391

Cited by 12 publications

(5 citation statements)

References 35 publications

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“…To overcome this shortage, machine learning is widely used to address a range of nonlinear prediction problems, recently including the prediction of water quality [17][18][19]. A support vector machine (SVM) is a typical model that represents an advanced form of machine learning and shows remarkable performance.…”

Section: Introductionmentioning

confidence: 99%

Modeling Daily and Monthly Water Quality Indicators in a Canal Using a Hybrid Wavelet-Based Support Vector Regression Structure

Wang

Yuan

Pan

et al. 2020

Water

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Accurate prediction of water quality indicators plays an important role in the effective management of water resources. The models which studied limited water quality indicators in natural rivers may give inadequate guidance for managing a canal being used for water diversion. In this study, a hybrid structure (WA-PSO-SVR) based on wavelet analysis (WA) coupled with support vector regression (SVR) and particle swarm optimization (PSO) algorithms was developed to model three water quality indicators, chemical oxygen demand determined by KMnO4 (CODMn), ammonia nitrogen (NH3-N), and dissolved oxygen (DO), in water from the Grand Canal from Beijing to Hangzhou. Modeling was independently conducted over daily and monthly time scales. The results demonstrated that the hybrid WA-PSO-SVR model was able to effectively predict non-linear stationary and non-stationary time series and outperformed two other models (PSO-SVR and a standalone SVR), especially for extreme values prediction. Daily predictions were more accurate than monthly predictions, indicating that the hybrid model was more suitable for short-term predictions in this case. It also demonstrated that using the autocorrelation and partial autocorrelation of time series enabled the construction of appropriate models for water quality prediction. The results contribute to water quality monitoring and better management for water diversion.

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Section: Introductionmentioning

confidence: 99%

Modeling Daily and Monthly Water Quality Indicators in a Canal Using a Hybrid Wavelet-Based Support Vector Regression Structure

Wang

Yuan

Pan

et al. 2020

Water

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“…In this study, the SVR method was used to develop the surrogate model for numerical simulation of variable density seawater intrusion. The core idea and theory of the method is to map the input data into a high-dimensional space through a non-linear mapping function and perform linear regression analysis in the high-dimensional space (Ouyang et al, 2017;Liu et al, 2019).…”

Section: Support Vector Regression Surrogate Modelmentioning

confidence: 99%

Stochastic simulation of seawater intrusion in Longkou area, China

Fan¹,

Cui²,

et al. 2022

Preprint

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Seawater intrusion is a common groundwater pollution problem, which has a great impact on ecological environment and economic development. In this paper, a numerical simulation model of variable density groundwater was constructed to simulate and predict the future seawater intrusion in Longkou city, Shandong Province of China. The influence of the sensitive parameter uncertainty of the model on the simulation results was evaluated by using the Monte Carlo method. In order to reduce the computational load from repeatedly calling the simulation model, the surrogate model was established by using the Support Vector Regression (SVR) method. The research results indicate that the SVR surrogate model can fit the input-output relationship of the simulation model with high accuracy. The seawater intrusion in the Longkou area will gradually aggravate at a slow rate, and the area of seawater intrusion is estimated to be 67.45 to 77.07 km2 after 30 years with 80% confidence.

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“…AI models can fit nonlinear data well without detailed physical information, resulting in more accurate prediction results [ 13 , 14 ]. Artificial neural networks (ANNs) [ 15 ], fuzzy logic-based models [ 16 ], support vector machines (SVMs) [ 17 ], and support vector regression (SVR) machines [ 18 ] have been widely used to predict and assess water quality.…”

Section: Introductionmentioning

confidence: 99%

Research on SVR Water Quality Prediction Model Based on Improved Sparrow Search Algorithm

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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Multiparameter water quality trend prediction technique is one of the important tools for water environment management and regulation. This study proposes a new water quality prediction model with better prediction performance, which is combined with improved sparrow search algorithm (ISSA) and support vector regression (SVR) machine. For the problems of low population diversity and easily falling into local optimum of sparrow search algorithm (SSA), ISSA is proposed to increase the initial population diversity by introducing Skew-Tent mapping and to help the algorithm jump out of local optimum by using the adaptive elimination mechanism. The optimal values of the penalty factor C and kernel function parameter g of the SVR model are selected using ISSA to make the model have better prediction accuracy and generalization performance. The performance of the ISSA-SVR water quality prediction model is compared with BP neural network, SVR model, and other hybrid models by conducting water quality prediction experiments with actual breeding-water quality data. The experimental results showed that the prediction accuracy of the ISSA-SVR model was significantly higher than that of other models, reaching 99.2%; the mean square deviation (MSE) was 0.013, which was 79.37% lower than that of the SVR model and 75% lower than that of SSA-SVR model, and the coefficient of determination R 2 was 0.98, which was 5.38% higher than that of the SVR model and 7.57% higher than that of the SSA-SVR model, indicating that the ISSA-SVR water quality prediction model has some engineering application value in the field of water body management.

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Optimizing the Water Treatment Design and Management of the Artificial Lake with Water Quality Modeling and Surrogate-Based Approach

Cited by 12 publications

References 35 publications

Modeling Daily and Monthly Water Quality Indicators in a Canal Using a Hybrid Wavelet-Based Support Vector Regression Structure

Modeling Daily and Monthly Water Quality Indicators in a Canal Using a Hybrid Wavelet-Based Support Vector Regression Structure

Stochastic simulation of seawater intrusion in Longkou area, China

Research on SVR Water Quality Prediction Model Based on Improved Sparrow Search Algorithm

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