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Machine Learning Approaches for Predicting Arsenic Adsorption from Water Using Porous Metal–organic Frameworks by Jafar Abdi; Golshan Mazloom is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Abstract Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, its eliminating is highly necessary. In this study, the ability of different novel and robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Decision Tree, and Random Forest was implemented to predict the adsorptive removal of arsenate [As(V)] from wastewater over 13 different metal–organic frameworks (MOFs). A large experimental dataset was collected under various conditions. The adsorbent dosage, contact time, initial arsenic concentration, adsorbent surface area, temperature, solution pH, and the presence of anions were considered as input variables, and adsorptive removal of As(V) was selected as the output of the models. The developed models were evaluated using various statistical criteria. The obtained results indicated that the LightGBM model provided the most accurate and reliable response to predict As(V) adsorption by MOFs and possesses R^2^, RMSE, STD, and AAPRE (%) of 0.9958, 2.0688, 0.0628, and 2.88, respectively. The expected trends of As(V) removal with increasing initi

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Author

Jafar Abdi; Golshan Mazloom

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)