Evidence map›Paper›PMID 34735705›Full record

ArticleEnvironmental science and pollution research international2022

Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches.

Khalifa M Alkindi, Kaustuv Mukherjee, Manish Pandey, Aman Arora, Saeid Janizadeh, Quoc Bao Pham, Duong Tran Anh, Kourosh Ahmadi

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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 1 paper.

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

What it found

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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

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Khalifa M AlkindiUNESCO Chair on Aflaj Studies, Archaeohydrology, University of Nizwa, Nizwa, Oman.
Kaustuv MukherjeeDepartment of Geography, Chandidas Mahavidyalaya, Birbhum, WB, 731215, India.
Manish PandeyUniversity Center for Research & Development (UCRD), Chandigarh University, Mohali, 140413, Punjab, India.
Aman AroraDepartment of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 10025, Delhi, India.
Saeid JanizadehDepartment of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, 14115-111, Tehran, Iran.
Quoc Bao PhamInstitute of Applied Technology, Thu Dau Mot University, Binh Duong Province, Vietnam.
Duong Tran AnhHo Chi Minh City University of Technology (HUTECH) 475A, Dien Bien Phu, Ward 25, Binh Thanh District, Ho Chi Minh City, Vietnam. ta.duong@hutech.edu.vn.
Kourosh AhmadiDepartment of Forestry, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, 14115-111, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nitrate is a major pollutant in groundwater whose main source is municipal wastewater and agricultural activities. In the present study, Bayesian approaches such as Bayesian generalized linear model (BGLM), Bayesian regularized neural network (BRNN), Bayesian additive regression tree (BART), and Bayesian ridge regression (BRR) were used to model groundwater nitrate contamination in a semiarid region Marvdasht watershed, Fars province, Iran. Eleven groundwater (GW) nitrate conditioning factors have been taken as input parameters for predictive modeling. The results showed that the Bayesian models used in this study were all competent to model groundwater nitrate and the BART model with R

Indexed as

GroundwaterWater Pollutants, ChemicalArtificial IntelligenceBayes TheoremEnvironmental MonitoringNitratesNitratesWater Pollutants, ChemicalBayesian additive regression treeBayesian approachGroundwater nitrate contaminationPollution monitoring

Identifiers

PMID34735705

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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.