Machine learning modelling of a nonlinear environmental index with sensitivity analysis for groundwater assessment.

Aliasghar Azma, Yakun Liu, Eden Tatek Alemu

Abstract read

In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Aliasghar AzmaSchool of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, 116024, China.

Yakun LiuSchool of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, 116024, China.

Eden Tatek AlemuHydraulic and Water Resources Engineering, Wolaita Sodo University, P.O.Box: 138, Wolaita Sodo, Ethiopia. etau002020212@outlook.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to evaluate groundwater quality the Marvdasht aquifer using the Groundwater Quality Index (GWQI), which was determined using a conventional method and also predicted using three machine learning algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF). For this purpose, groundwater quality parameters (pH, EC, TDS, TH, Na⁺, Ca

Indexed as

ANNGroundwater qualityGWQIRandom forest

Identifiers

PMID41872349

DOI10.1038/s41598-026-44906-6

PMCPMC13153407

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

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.