Development of SVR-based model and comparative analysis with MLR and ANN models for predicting the sorption capacity of Cr(VI)

Parveen, Nusrat; Zaidi, Sadaf; Danish, Mohammad

doi:10.1016/j.psep.2017.03.007

Cited by 75 publications

(23 citation statements)

References 29 publications

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“…It consists of: (1) input layer (independent variables), (2) hidden layers and, (3) the output layer (dependent variable) [7].…”

Section: Analysis Of Ann Modelmentioning

confidence: 99%

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Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting)

Siregar

Wanto

2017

IJISTECH

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“…It consists of: (1) input layer (independent variables), (2) hidden layers and, (3) the output layer (dependent variable) [7].…”

Section: Analysis Of Ann Modelmentioning

confidence: 99%

“…On the other hand, ANN models are based on the empirical risk minimization (ERM) principle which only minimizes the empirical error and does not consider the capacity of the learning machines [7].…”

Section: Accuracymentioning

confidence: 99%

Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting)

Siregar

Wanto

2017

IJISTECH

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“…The variability that is exhibited by the response variable has two components; a systematic and random part. MLR equation is a weighted linear combination of the independent variables [11][12][13][14].Very few studies have explored the use of MLR model for landfill leachate treatment. Bhat et al [15] developed equations for estimating biological oxygen demand (BOD) and chemical oxygen demand (COD) removal from landfill leachate.…”

Section: Introductionmentioning

confidence: 99%

Removal of COD from landfill leachate by Predication and Evaluation of Multiple Linear Regression (MLR) Model and Fenton process

et al. 2019

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M UNICIPAL Solid Waste (MSW) landfill leachate contains highly concentrated organic substances which are hazardous to the environment. Therefore, it must be treated before discharged into water bodies and suitable techniques are essential for effective treatment. In this regard, multiple linear regression (MLR) model has shown to be a favorable technique for optimization of landfill leachate treatment. In this study, four operational variables, H 2 O 2 :Fe 2+ ratio, pH, reaction time, and Fe 2+ concentration, were assessed using the model. The chemical oxygen demand (COD) removal was 96.43% by experiment, while the predicted assessment was 100% under the optimized settings; time = 33.87 min, concentration of Fe 2+ = 749.64 mg/L, pH = 3, and ratio of H 2 O 2 :Fe 2+ = 2 during Fenton treatment. The high value of the coefficient of determination, R 2 = 0.896, designates a resilient relationship between the experimental and model values. The residual study (residual plot) specified that the points were randomly distributed, confirming the appropriateness of the model. The MLR model established may possibly be used for assessment of other landfill leachate treatment.

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“…Chen et al [11] used the BP algorithm to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Parveen et al [12] applied Grey-Markov process prediction method to analyze the trend of several major ionic concentrations in Jilantai salt lake brine. Based on the combination of Grey system and Markov process, the prediction accuracy of data with large random fluctuation was improved.…”

Section: Introductionmentioning

confidence: 99%

Modeling method of prediction model for salt field ion concentration under solar thermal system using random forest

Liu

Zhang

et al. 2019

THERM SCI

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At present, the underground brine deposit of Lop Nor salt lake in Xinjiang, while is rich in solar energy resource and can be one high efficient solar thermal utilization area, has become an important potash production base in China. During the development of salt lake brine, the collection methods are different according to the concentration of ions in different locations. In order to solve the problems of low accuracy and high calculation cost in prediction of salt field ion concentration, a data mining method based on random forest is applied in this paper. To build the model, we collected K + , SO 4 2-, Cl-, and other two kinds of ions, among which the features included the collection time, collection locality and the number of salt pond. We used several methods to train and test the sample data, evaluated the experimental results using a variety of performance metrics and compared it with other methods at the same time. The results revealed that the optimal random forest model yielded the mean square error and coefficient of determination values of 0.073 and 0.940, which performed relatively better than support vector machine and extremely randomized trees.

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Development of SVR-based model and comparative analysis with MLR and ANN models for predicting the sorption capacity of Cr(VI)

Cited by 75 publications

References 29 publications

Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting)

Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting)

Removal of COD from landfill leachate by Predication and Evaluation of Multiple Linear Regression (MLR) Model and Fenton process

Modeling method of prediction model for salt field ion concentration under solar thermal system using random forest

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