articleScientific ReportsMar 23, 2026GOLD OA

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

AAAliasghar AzmaYLYakun LiuETEden Tatek Alemu

Dalian University of Technology · Wolaita Sodo University

PubMed
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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⁺, Ca2⁺, Mg2⁺, Cl⁻, HCO₃⁻, SO₄2⁻, and K⁺) were measured. The GWQI was calculated based on WHO drinking-water standards, and a spatial map was generated in ArcGIS. Results showed that TDS, EC, Na⁺, Cl⁻, and Mg2⁺ were the most influential parameters controlling groundwater quality. The groundwater quality was classified as hard to…

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4
total citations
FWCI
41.49
Percentile
99%
References
34
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3

Topics & keywords

Topics
Keywords
  • Groundwater
  • Groundwater recharge
  • Aquifer
  • Support vector machine
  • Water quality
  • Artificial neural network
  • Hydrology (agriculture)
  • Sensitivity (control systems)
UN Sustainable Development Goals
  • Clean water and sanitation
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