ArticlePloS one2021
Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Data driven water quality assessment using machine learning and synthetic data generation.Scientific reports · 2026Article
- Groundwater quality index prediction and aquifer failure risk analysis using metaheuristic-tuned artificial neural networks.Scientific reports · 2026Article
- Machine learning-based monitoring and design of managed aquifer rechargers for sustainable groundwater management: scope and challenges.Environmental science and pollution research international · 2025Review
- Development of the machine learning and deep learning models with SHAP strategy for predicting groundwater levels in South Korea.Scientific reports · 2025Article
- Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique.Scientific reports · 2024Article
- Groundwater quality characterization using an integrated water quality index and multivariate statistical techniques.PloS one · 2024Article
- Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh.Scientific reports · 2022Article
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7 authors.
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Abstract
Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.
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