ArticleScientific reports2022
Machine learning approaches for predicting arsenic adsorption from water using porous metal-organic frameworks.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 62 citations in OpenAlex.
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- Metal-organic frameworks (MOFs) for arsenic remediation: a brief overview of recent progress.RSC advances · 2025Review
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- Predicting removal of arsenic from groundwater by iron based filters using deep neural network models.Scientific reports · 2024Article
- Efficient arsenic removal from water using iron-impregnated low-temperature biochar derived from henequen fibers: performance, mechanism, and LCA analysis.Scientific reports · 2024Article
- A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks.Nature communications · 2024Article
- Exploring Optimization of Zeolites as Adsorbents for Rare Earth Elements in Continuous Flow by Machine Learning Techniques.Molecules (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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
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