Improved neural network with least square support vector machine for wastewater treatment process

Zhu, Jiaan; Jiang, Zhenzhen; Li, Feng

doi:10.1016/j.chemosphere.2022.136116

Cited by 29 publications

(7 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… While R 2 indicates how well the datasets fit on the regression model, MSE and RMSE are the standard deviation (SD) of the predicted error and are indicators of how well the data points are distributed around the line of best fit. The R 2 , MSE, and RMSE were calculated using eqs , , and , respectively R 2 = 1 − [ ∑ i = 1 n ( y i − x i ) 2 ∑ i = 1 n ( y − y i true̅ ) 2 ] MSE = 1 n ∑ i = 1 i = n ( y i − f false( x i false) 2 RMSE = ∑ i = 1 n ( false( y i − x i false) 2 n ) where f ( x i ) is the predicted output value, y i is the true value, y ̅ i is the average observed value, x i is the prediction value by the algorithm, and n is the number of observations.…”

Section: Methodsmentioning

confidence: 99%

“…Support vector machines (SVMs), which are used to predict discrete values, are based on a phenomenon similar to the support vector regression (SVR) technique. The SVR is a multipurpose algorithm model that can be used for linear or nonlinear prediction of datasets. , The mechanism of the SVM is dependent on the training data subset. For developing the hyperplane, datasets outside the decision boundary were eliminated (outlier removed) to prevent overfitting issues .…”

Section: Methodsmentioning

confidence: 99%

“…The SVR is a multipurpose algorithm model that can be used for linear or nonlinear prediction of datasets. 33,35 The mechanism of the SVM is dependent on the training data subset. For developing the hyperplane, datasets outside the decision boundary were eliminated (outlier removed) to prevent overfitting issues.…”

Section: Prediction Of Kmentioning

confidence: 99%

“…While R 2 indicates how well the ACS https://doi.org/10.1021/acsestwater.4c00121 ACS EST Water XXXX, XXX, XXX−XXX i n d i v i d u a l n o t d i r e c t l y a s s o c i a t e d w i t h s c i t e i s p r o h i b i t e d .datasets fit on the regression model, MSE and RMSE are the standard deviation (SD) of the predicted error and are indicators of how well the data points are distributed around the line of best fit. The R 2 , MSE, and RMSE were calculated using eqs 3, 4, and 5, respectively 33. …”

mentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands

Singh,

Soti,

Kulshreshtha

et al. 2024

ACS EST Water

View full text Add to dashboard Cite

Land area optimization for horizontal flow constructed wetlands (HFCWs) with a low organic loading rate (OLR) needs special considerations as the microflora changes dramatically with the OLR. The P-k-C* approach does not lead to an accurate calculation of k-values in these wetlands. In this research, nonlinear machine learning models [Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANN)] are applied to predict realistic k-values. Data from 37 low-OLR HFCWs (n = 544) were analyzed, and the k-values calculated for these wetlands were found to vary markedly (0.059−0.249 with an average of 0.113 ± 0.090 m/day). The classification of k-values based on the OLR, applied loading rate, and media depth leads to the reduction in standard deviations (SDs) from 83.40 to 35.27%. kvalues with the least SDs are needed for optimal design for low-OLR CWs. The SVR, RF, and ANN models were tested, and the best prediction efficiency on testing datasets was achieved through the ANN model with R 2 (k TKN )= 0.768 (RMSE = 0.067) for total Kjeldahl nitrogen (TKN), R 2 (k TN )= 0.835 (RMSE = 0.043) for total nitrogen (TN), and R 2 (k TP ) = 0.723 (RMSE = 0.087) for total phosphorus (TP). The outcome was validated using primary data from HFCWs, which also confirmed the superiority of the ANNbased model, which can be used for design customization of low-OLR HFCWs.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Prediction Of Kmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands

Singh,

Soti,

Kulshreshtha

et al. 2024

ACS EST Water

View full text Add to dashboard Cite

show abstract

“…They found that the LSTM model performs better than the DNN model, and a hybrid model combining mechanistic with DL models is helpful in quantitatively describing and understanding complex N 2 O emission dynamics from WWTPs. Zhu, Jiang and Feng [37] also proposed an upgraded feedforward NN with the least square SVM (FFNN-LSSVM) method to forecast the effluent BOD/NH3-N of a WWTP. The proposed model has high predictive accuracy, limited computation duration and a simple calculation mechanism, and performs better than existing techniques in wastewater quality prediction.…”

Section: Process Parameters Optimizationmentioning

confidence: 99%

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Wang,

Cheng,

Liu

et al. 2023

Sustainability

View full text Add to dashboard Cite

In recent years, artificial intelligence (AI), as a rapidly developing and powerful tool to solve practical problems, has attracted much attention and has been widely used in various areas. Owing to their strong learning and accurate prediction abilities, all sorts of AI models have also been applied in wastewater treatment (WWT) to optimize the process, predict the efficiency and evaluate the performance, so as to explore more cost-effective solutions to WWT. In this review, we summarize and analyze various AI models and their applications in WWT. Specifically, we briefly introduce the commonly used AI models and their purposes, advantages and disadvantages, and comprehensively review the inputs, outputs, objectives and major findings of particular AI applications in water quality monitoring, laboratory-scale research and process design. Although AI models have gained great success in WWT-related fields, there are some challenges and limitations that hinder the widespread applications of AI models in real WWT, such as low interpretability, poor model reproducibility and big data demand, as well as a lack of physical significance, mechanism explanation, academic transparency and fair comparison. To overcome these hurdles and successfully apply AI models in WWT, we make recommendations and discuss the future directions of AI applications.

show abstract

Smart Plant‐Based Wastewater Treatment for Agricultural Irrigation Systems

Saha,

Biswas

2024

Smart Agritech

View full text Add to dashboard Cite

Improved neural network with least square support vector machine for wastewater treatment process

Cited by 29 publications

References 49 publications

Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands

Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands

A Review on Applications of Artificial Intelligence in Wastewater Treatment

Smart Plant‐Based Wastewater Treatment for Agricultural Irrigation Systems

Contact Info

Product

Resources

About