Journal Article

# Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model

04 Mar 2021-Chemosphere (Pergamon)-Vol. 276, pp 130162-130162
Abstract: Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.

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Open accessJournal Article
26 Jul 2021-IEEE Access
Abstract: An accurate prediction of water quality (WQ) related parameters is considered as pivotal decisive tool in sustainable water resources management. In this study, five different ensemble machine learning (ML) models including Quantile regression forest (QRF), Random Forest (RF), radial support vector machine (SVM), Stochastic Gradient Boosting (GBM) and Gradient Boosting Machines (GBM_H2O) were developed to predict the monthly biochemical oxygen demand (BOD) values of the Euphrates River, Iraq. For this aim, monthly average data of water temperature (T), Turbidity, pH, Electrical Conductivity (EC), Alkalinity (Alk), Calcium (Ca), chemical oxygen demand (COD), Sulfate (SO4), total dissolved solids (TDS), total suspended solids (TSS), and BOD measured for ten years period were used in this study. The performances of these standalone models were compared with integrative models developed by coupling the applied ML models with two different feature extraction algorithms i.e., Genetic Algorithm (GA) and Principal Components Analysis (PCA). The reliability of the applied models was evaluated based on the statistical performance criteria of determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe model efficiency coefficient (NSE), Willmott index (d), and percent bias (PBIAS). Results showed that among the developed models, QRF model attained the superior performance. The performance of the evaluated models presented in this study proved that the developed integrative PCA-QRF model presented much better performance compared with the standalone ones and with those integrated with GA. The statistical criteria of R2, RMSE, MAE, NSE, d, and PBIAS of PCA-QRF were 0.94, 0.12, 0.05, 0.93, 0.98, and 0.3, respectively.

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Open accessJournal Article
09 Oct 2021-Polymers

Open accessJournal Article
Hai Tao1, Hai Tao2, Hai Tao3, Zainab S. Al-Khafaji4  +20 moreInstitutions (23)
Abstract: River sedimentation is an important indicator for ecological and geomorphological assessments of soil erosion within any watershed region. Sediment transport in a river basin is therefore a multifa...

Topics: Sedimentation (59%), Drainage basin (56%), Sediment transport (55%)

Open accessJournal Article
Abstract: Owing to their persistent nature, pharmaceutical products (PPs) are emerging as potent water pollutants. Here, experimental and data-driven modeling, specifically multilayer perceptron (MLP) neural networking and gene expression programming (GEP), was employed to predict the removal of the most common antihypertensive and antibiotic drugs, namely propranolol and trimethoprim, from reclaimed water (RW) through a managed aquifer recharge system (MARS). The characteristics of RW and soil used as the column medium, including operating time (days); pH; dissolved organic carbon; electrical conductivity; and concentration of nitrogen dioxide, nitrate, sulfate, ferrous, chloride, and manganese, were included as the input parameters and removal of the selected PPs as the model output. A dataset was created through an experimental study conducted over a year of continuous operation of MARS to predict the removal of the selected PPs. MLP and GEP models were developed for one of the selected PPs and tested for the other to determine model reliability. The developed models were assessed using statistical performance matrices. The experimental results showed over 80% propranolol and trimethoprim removal from RW through MARS. The proposed GEP predictive models for propranolol and trimethoprim removal showed higher accuracy (R2 = 0.91 and 0.87, respectively) than the MLP models (R2 = 0.827 and 0.756, respectively). Therefore, the proposed GEP models provide better predictions and mathematical relationships for future studies. Thus, data-driven machine learning models can predict the removal of specific PPs from RW through MARS and minimize the experimental workload.

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Open accessJournal Article
Leo Breiman1Institutions (1)
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Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.

58,232 Citations

Open accessBook
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38,164 Citations

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Topics:

36,497 Citations

Open accessJournal Article
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Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

35,157 Citations

Open accessJournal Article
Abstract: We discuss the following problem given a random sample X = (X 1, X 2,…, X n) from an unknown probability distribution F, estimate the sampling distribution of some prespecified random variable R(X, F), on the basis of the observed data x. (Standard jackknife theory gives an approximate mean and variance in the case R(X, F) = $$\theta \left( {\hat F} \right) - \theta \left( F \right)$$, θ some parameter of interest.) A general method, called the “bootstrap”, is introduced, and shown to work satisfactorily on a variety of estimation problems. The jackknife is shown to be a linear approximation method for the bootstrap. The exposition proceeds by a series of examples: variance of the sample median, error rates in a linear discriminant analysis, ratio estimation, estimating regression parameters, etc.