Predicting removal of arsenic from groundwater by iron based filters using deep neural network models.

Muhammad Ali Martuza, Md Shafiquzzaman, Husnain Haider, Amimul Ahsan, Abdelkader T Ahmed

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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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2 · The registry

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Muhammad Ali MartuzaDepartment of Computer Engineering, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia.

Md ShafiquzzamanDepartment of Civil Engineering, College of Engineering, Qassim University, Buraydah, 51452, Saudi Arabia. M.UzzaMan@qu.edu.sa.

Husnain HaiderDepartment of Civil Engineering, College of Engineering, Qassim University, Buraydah, 51452, Saudi Arabia.

Amimul AhsanDepartment of Civil and Environmental Engineering, Islamic University of Technology (IUT), Gazipur, 1704, Bangladesh.

Abdelkader T AhmedCivil Engineering Department, Faculty of Engineering, Islamic University of Madinah, Madinah, 42351, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Arsenic (As) contamination in drinking water has been highlighted for its environmental significance and potential health implications. Iron-based filters are cost-effective and sustainable solutions for As removal from contaminated water. Applying Machine Learning (ML) models to investigate and optimize As removal using iron-based filters is limited. The present study developed Deep Learning Neural Network (DLNN) models for predicting the removal of As and other contaminants by iron-based filters from groundwater. A small Original Dataset (ODS) consisting of 20 data points and 13 groundwater parameters was obtained from the field performances of 20 individual iron-amended ceramic filters. Cubic-spline interpolation (CSI) expanded the ODS, generating 1600 interpolated data points (IDPs) without duplication. The Bayesian optimization algorithm tuned the model hyper-parameters and IDPs in a Stratified fivefold Cross-Validation (CV) setup trained all the models. The models demonstrated reliable performances with the coefficient of determination (R

Indexed as

ArsenicArtificial intelligenceDeep learning neural networksGroundwaterIron-based filterPollutant removal

Identifiers

PMID39488582

DOI10.3390/app12030999

PMCPMC11531467

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.