Evidence map›Paper›PMID 29198027›Full record

ArticleEnvironmental science and pollution research international2018

Estimation of sodium adsorption ratio indicator using data mining methods: a case study in Urmia Lake basin, Iran.

Mohammad Taghi Sattari, Arya Farkhondeh, John Patrick Abraham

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Article in Environmental science and pollution research international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Mohammad Taghi SattariDepartment of Water Engineering, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran. mtsattar@gmail.com.
Arya FarkhondehDepartment of Water Engineering, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.
John Patrick AbrahamSchool of Engineering, University of St. Thomas, Minnesota, 2115 Summit Avenue, St. Paul, MN, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Water quality is a major concern around the world, particularly in dry climates. Usually, assessment of surface water quality is costly and time-consuming. In this situation, a method which could estimate the water quality accurately with the minimum of hydro-chemical parameters would be appealing. In this study, three data mining methods, namely, M5 model tree, support vector machine (SVM), and Gaussian process (GP), were employed to estimate the sodium adsorption ratio (SAR) indicator in the Shahrchay River located in the west of the Urmia Lake basin, Iran. Results from these methods were compared with an artificial neural network (ANN). Different hydro-chemical parameters were assessed and the most effective parameters were selected. Five combinations of the selected parameters were developed as input parameters to the models. The results indicated that the M5 model tree has a superior performance among the data mining methods, where the combination of sodium and electrical conductivity (Na and EC) is used as input parameters, with a coefficient of determination (R

Indexed as

AdsorptionAgricultural IrrigationData MiningDesert ClimateEnvironmental MonitoringIranLakesNeural Networks, ComputerRiversSodiumSupport Vector MachineWater QualitySodiumGaussian process (GP)M5 model treeSodium adsorption ratioSupport vector machine (SVM)Urmia Lake basinWater quality

Identifiers

PMID29198027

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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.