Evidence map›Paper›PMID 34499306›Full record

ArticleEnvironmental science and pollution research international2022

Predictive model for progressive salinization in a coastal aquifer using artificial intelligence and hydrogeochemical techniques: a case study of the Nile Delta aquifer, Egypt.

Ahmed M Nosair, Mahmoud Y Shams, Lobna M AbouElmagd, Aboul Ella Hassanein, Alan E Fryar, Hend S Abu Salem

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Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ahmed M NosairEnvironmental Geophysics Lab (ZEGL), Geology Department, Faculty of Science, Zagazig University, Zagazig, Egypt.ORCID http://orcid.org/0000-0001-5504-616X
Mahmoud Y ShamsFaculty of Artificial Intelligence, Kafrelsheikh University, KafrelSheikh, 33511, Egypt.ORCID http://orcid.org/0000-0003-3021-5902
Lobna M AbouElmagdMisr Higher Institute for Commerce and Computers, MET, Mansoura, Egypt.ORCID http://orcid.org/0000-0002-1247-8373
Aboul Ella HassaneinFaculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt.ORCID http://orcid.org/0000-0002-9989-6681
Alan E FryarDepartment of Earth and Environmental Sciences, University of Kentucky, Lexington, USA. alan.fryar@uky.edu.ORCID http://orcid.org/0000-0003-2586-6858
Hend S Abu SalemGeology Department, Faculty of Science, Cairo University, Cairo, Egypt.ORCID http://orcid.org/0000-0002-8758-1965

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To monitor groundwater salinization due to seawater intrusion (SWI) in the aquifer of the eastern Nile Delta, Egypt, we developed a predictive regression model based on an innovative approach using SWI indicators and artificial intelligence (AI) methodologies. Hydrogeological and hydrogeochemical data of the groundwater wells in three periods (1996, 2007, and 2018) were used as input data for the AI methods. All the studied indicators were enrolled in feature extraction process where the most significant inputs were determined, including the studied year, the distance from the shoreline, the aquifer type, and the hydraulic head. These inputs were used to build four basic AI models to get the optimal prediction results of the used indicators (the base exchange index (BEX), the groundwater quality index for seawater intrusion (GQI

Indexed as

Artificial IntelligenceGroundwaterEgyptEnvironmental MonitoringSeawaterArtificial intelligenceFeedforward backpropagation neural networkGroundwater contaminationHydrogeochemical analysisRegression modelsSeawater intrusion

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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.