Modeling the organic matter of water using the decision tree coupled with bootstrap aggregated and least-squares boosting

Tahraoui, Hichem; Amrane, Abdeltif; Belhadj, Abd-Elmouneïm; Zhang, Jie

doi:10.1016/j.eti.2022.102419

Cited by 29 publications

(27 citation statements)

References 39 publications

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“…In order to assess the performance of the ANN, the mean square error (MSE) was calculated to demonstrate the statistical difference between the predicted and experimental values [ 12 , 15 ]. The accuracy of the developed model was assessed by the value of the correlation coefficient R 2 [ 20 , 21 , 22 , 23 , 24 , 25 ].…”

Section: Methodsmentioning

confidence: 99%

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

et al. 2023

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The main objective of this study was to create a mathematical tool that could be used with experimental data to predict the rheological flow behavior of functionalized xanthan gum according to the types of chemical groups grafted onto its backbone. Different rheological and physicochemical analyses were applied to assess six derivatives synthesized via the etherification of xanthan gum by hydrophobic benzylation with benzyl chloride and carboxymethylation with monochloroacetic acid at three (regent/polymer) ratios R equal to 2.4 and 6. Results from the FTIR study verified that xanthan gum had been modified. The degree of substitution (DS) values varying between 0.2 and 2.9 for carboxymethylxanthan gum derivatives were found to be higher than that of hydrophobically modified benzyl xanthan gum for which the DS ranged from 0.5 to 1. The molecular weights of all the derivatives were found to be less than that of xanthan gum for the two types of derivatives, decreasing further as the degree of substitution (DS) increased. However, the benzyl xanthan gum derivatives presented higher molecular weights varying between 1,373,146 (g/mol) and 1,262,227 (g/mol) than carboxymethylxanthan gum derivatives (1,326,722–1,015,544) (g/mol). A shear-thinning behavior was observed in the derivatives, and the derivatives’ viscosity was found to decrease with increasing DS. The second objective of this research was to create an ANN model to predict one of the rheological properties (the apparent viscosity). The significance of the ANN model (R2 = 0.99998 and MSE = 5.95 × 10−3) was validated by comparing experimental results with the predicted ones. The results showed that the model was an efficient tool for predicting rheological flow behavior.

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Section: Methodsmentioning

confidence: 99%

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

et al. 2023

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“…The GBDT also ts the error residual of the previous tree into a decision tree. Therefore, every new tree in the community is focused on reducing the error made by the previous weak learner rather than predicting the target directly (Inria 2022;Tahraoui et al 2022). The aforementioned structure of the GDBT is very useful for spatial distribution models of soil properties, that are highly affected by environmental factors such as organic carbon and have high spatial variation, under the number of covariates is limited.…”

Section: Gradient Boosted Decision Treementioning

confidence: 99%

Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent–boosted regression tree

Budak

Günal²,

Kılıç

et al. 2023

Environ Sci Pollut Res

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Carbon sequestration in earth surface is higher than the atmosphere, and the amount of carbon stored in wetlands is much greater than all other land surfaces. The purpose of this study was to estimate soil organic carbon stocks (SOCS) and investigate spatial distribution pattern of Yuksekova wetlands and surrounding lands in Hakkari province of Turkey using machine learning and remote sensing data. Total carbon stock in study area was calculated at 10-cm vertical resolution in 0 to 30 cm depth for 50 sampling locations. Vegetation, soil and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Signi cant correlations were obtained between the indices and SOCS, thus, the remote sensing indices were used as covariates in Multi-Layer Perceptron Neural Network (MLP) and Gradient Descent Boosted Regression Tree (GBDT) machine learning models. Mean Absolute Error, Root Mean Square Error and Mean Absolute Percentage Error were 3.94 (Mg C ha − 1 ), 6.64 (Mg C ha − 1 ) and 9.97%, respectively. The Simple Ratio Clay Index (SRCI), which represents the soil texture, was the most important factor in the SOCS estimation variance. In addition, the relationship between SRCI and Topsoil Grain Size Index revealed that topsoil clay content is a highly important parameter in spatial variation of SOCS. The spatial SOCS values obtained using the GBDT model and the mean SOCS values of the CORINE land lover classes were signi cantly different. The wetlands had the highest SOCS (61.46 Mg C ha − 1 ), followed by the lands principally occupied by natural vegetation and used as rangelands around the wetland (50.22 Mg C ha − 1 ). Environmental conditions have signi cant effect on SOCS which has high spatial variation in the study area. Reliable spatial SOCS information was obtained with the combination of Sentinel-2 guided multi-index remote sensing modeling strategy and the GBDT model. Therefore, the spatial estimation of SOCS can be successfully carried out with up-to-date machine learning algorithms only using remote sensing data. Reliable estimation of SOCS in wetlands and surrounding lands can help understand policy and decision makers the importance of wetlands in mitigating the negative impacts of global warming .

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“…The quality of the developed models was examined using statistical analysis and ANOVA at a 95% confidence level. Various model quality measures, such as the p-value, F-value, degree of freedom (DF), coefficient of determination (R 2 ), adjusted determination of coefficient (R adj 2 ), and Root Mean Square Error (RMSE), were used to evaluate the statistical adequacy of the models [15,25,[27][28][29][30][31][32][33][34][35]. The F-value describes the variation in the responses, which can be evaluated using a regression equation, whereas the p-value indicates the statistical adequacy of the developed model.…”

Section: Statistical Evaluation Criteriamentioning

confidence: 99%

Designing an Efficient Surfactant–Polymer–Oil–Electrolyte System: A Multi-Objective Optimization Study

et al. 2023

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This research aimed to study the effects of individual components on the physicochemical properties of systems composed of surfactants, polymers, oils, and electrolytes in order to maximize the recovery efficiency of kerosene while minimizing the impact on the environment and human health. Four independent factors, namely anionic surfactant sodium dodecylbenzene sulfonate (X1) (SDBS), oil (X2) (kerosene), water-soluble polymer poly(ethylene glycol) (X3) (PEG), and sodium chloride (X4) (NaCl), were studied using the full factorial design (FFD) model. Four output variables, namely conductivity (Y1), turbidity (Y2), viscosity (Y3), and interfacial tension (IFT) (Y4), were taken as the response variables. All four FFD models have high coefficients of determination and low errors. The developed models were used in a multi-objective optimization (MOO) framework to determine the optimal conditions. The obtained optimal conditions are X1 = 0.01, X2 = 50, X3 = 5, and X4 = 0.1, with an error of 0.9414 between the predicted and experimental objective function values. This result shows the efficiency of the model developed and the system used for the recovery of kerosene, while also having a positive effect on the protection of the environment.

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Modeling the organic matter of water using the decision tree coupled with bootstrap aggregated and least-squares boosting

Cited by 29 publications

References 39 publications

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent–boosted regression tree

Designing an Efficient Surfactant–Polymer–Oil–Electrolyte System: A Multi-Objective Optimization Study

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