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A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms
Applications of machine learning algorithms (MLAs) to modeling the adsorption efficiencies of different heavy metals have been limited by the adsorbate–adsorbent pair and the selection of specific MLAs. In the current study, adsorption efficiencies of fourteen heavy metal–adsorbent (HM-AD) pairs were modeled with a variety of ML models such as support vector regression with polynomial and radial basis function kernels, random forest (RF), stochastic gradient boosting, and bayesian additivedoi:10.3390/w12123490 fatcat:agelmyk6lvfl5adyh2e7m3ynzm