Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models

• Published in: Environmental science and pollution research international Vol. 28; no. 24; pp. 31670 - 31688
• Main Authors: Bhagat, Suraj Kumar, Paramasivan, Mariapparaj, Al-Mukhtar, Mustafa, Tiyasha, Tiyasha, Pyrgaki, Konstantina, Tung, Tran Minh, Yaseen, Zaher Mundher
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Adsorption; Aquatic Pollution; Artificial Intelligence; Atmospheric Protection/Air Quality Control/Air Pollution; Clay; Comparative analysis; Correlation analysis; Dosage; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Feasibility Studies; Heavy metals; Intelligence; Lead; Magnesium Compounds; Mathematical models; Metal concentrations; Parameters; pH effects; Prediction models; Principal components analysis; Research Article; Silicon Compounds; Stochasticity; Support vector machines; Waste Water Technology; Water Management; Water Pollution Control; Attapulgite clay; Copper adsorption; MARS model; Artificial intelligence; Prediction
• ISSN: 0944-1344; 1614-7499
• DOI: 10.1007/s11356-021-12836-7

This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite clay. Models are constructed using batch stochastic data of heavy metal (HM) concentrations under different physicochemical conditions. Implementation of auto-hyper-parameter tuning using grid-search approach and comparative analysis is performed against the benchmark artificial intelligence (AI) models. Models are constructed based on Pb concentration (IC), the dosage of attapulgite clay (dose), contact time (CT), pH, and NaNO 3 (SN). Principle component analysis (PCA) and correlation analysis (CA) methods are integrated to assess the importance of the applied predictors and their relationship with the target. Research findings approved the potential of the grid-RF model as a marginal superior predictive model against the grid-SVM in terms of MAE, i.e., 3.29 and 3.34, respectively; moreover, the md scored the same, i.e., 0.93, which reveals the potential predictability for both. Nonetheless, grid-MARS and standalone MARS models remained likewise in their predictability. IC parameter demonstrated the highest influential among all the predictors with the highest value of importance in the case of all three evaluators. The solution pH and dose stands together with marginal differences in case of PCA method; however, solution pH and CT appeared with similarity impact using the PCA method. Graphical abstract