Τίτλος:
Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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 NaNO3 (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: [Figure not available: see fulltext.]. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.
Συγγραφείς:
Bhagat, S.K.
Paramasivan, M.
Al-Mukhtar, M.
Tiyasha, T.
Pyrgaki, K.
Tung, T.M.
Yaseen, Z.M.
Περιοδικό:
Environmental Science and Pollution Research
Εκδότης:
Springer Science and Business Media Deutschland GmbH
Λέξεις-κλειδιά:
adsorption; artificial intelligence; benchmarking; clay; lead; numerical model; stochasticity, attapulgite; lead; magnesium derivative; silicon derivative, adsorption; artificial intelligence; clay; feasibility study; intelligence, Adsorption; Artificial Intelligence; Clay; Feasibility Studies; Intelligence; Lead; Magnesium Compounds; Silicon Compounds
DOI:
10.1007/s11356-021-12836-7
