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Application of Artificial Intelligence Models for modeling Water Quality in Groundwater: Comprehensive Review, Evaluation and Future Trends


This study reported the state of the art of different artificial intelligence (AI) methods for groundwater quality (GWQ) modeling and introduce a brief description of common AI approaches. In addtion a bibliographic review of practices over the past two decades, was presented and attained result were compared. More than 80 journal articles from 2001 to 2021 were review in terms of characteristics and capabilities of developing methods, considering data of input-output, etc. From the reviewed studies, it could be concluded that in spite of various weaknesses, if the artificial intelligence approaches were appropriately built, they can effectively be utilized for predicting the GWQ in various aquifers. Because many steps of applying AI methods are based on trial-and-error or experience procedures, it’s helpful to review them regarding the special application for GWQ modeling. Several partial and general findings were attained from the reviewed studies that could deliver relevant guidelines for scholars who intend to carry out related work. Many new ideas in the associated area of research are also introduced in this work to develop innovative approaches and to improve the quality of prediction water quality in groundwater for example, it has been found that the combined AI models with metaheuristic optimization are more reliable in capturing the nonlinearity of water quality parameters. However, in this review few papers were found that used these hybrid models in GWQ modeling. Therefore, for future works, it is recommended to use hybrid models to more furthere investigation and enhance the reliability and accuracy of predicting in GWQ.

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Radial basis function


Multi-layer perceptron


Deep learning


Random forest


eXtreme gradient boosting

R2 :

Determination coefficient


Root mean squared error


Mean absolute relative error


Multilayer perceptron


Gene expression programming.


Sodium adsorption ratio


Mean absolute percentage error


Scatter index


Correlation coefficient


Mean square error


Mean absolute error


Fuzzy c-means


Grid partition


Particle swarm optimization.




Sodium absorption ratio


Bayesian networks


Mixtures of Truncated Exponentials


Extreme gradient boosting


Variance account for


Percent Average Estimation Error


Potential Salinity


Dynamic evolving neural-fuzzy inference system


Exchangeable Sodium Percentage


Support vector regression


Group method of data handling




Residual Sodium Carbonate


Relative Bias


Self-organized map


Average Normalized Error for Parameter Estimates


Locally weighted projection regression


Relevance vector machines


Bayesian neural network


Reduction of error


Index of agreement


Kohonen self-organizing features map


Fuzzy-GIS-based groundwater quality index


Active Set Support Vector Regression


Magnesium Adsorption Ratio


percent mean relative error


Groundwater quality index


Multivariate adaptive regression spline

M5 Tree:

M5 Tree model


Genetic Algorithm


Gene expression programming.


Total organic carbon


Nash-Sutcliffe efficiency


World health organization


Legates and McCabe index


Standard deviation ratio


Willmott index of agreement


Normalized error


Multiple linear regression


Structural equation modeling


Geographic information system


Fuzzy Clustering Technique


Ant colony optimization for continuous domains


Mean misclassification error


Average absolute relative error


generalized regression neural network


average squared error


Residual sodium carbonate


Probabilistic Support Vector Machine


Magnesium adsorption ratio


Kellys ratio


Back-propagation neural network


Differential evolution.


Gaussian Process


Random tree


Percent of bias.


Probabilistic support vector machines


Probabilistic neural networks


Dissolved oxygen


Total alkalinity


Percent of bias.


Biological oxygen demand


Least square support vector machine


Chemical oxygen demand


Self-organizing map


Feed forward neural network


Fuzzy neural network-based support vector regression


Coefficient of efficiency


Akaike information criterion


K-nearest neighbor


Wavelet neural network


Mamdani Fuzzy Inference System




Extreme learning machine


Multi- layer perceptron


Mean absolute bias error


Principal component regression


Bayesian regulation


Recharge rate




Abstraction average rate




Groundwater level


Aquifer thickness


Depth from the surface to well screen


Distance from sea shoreline


Total rainfall


Relative humidity


Minimum temperature


Boosted regression tree


Maximum temperature


Average temperature


Total Petroleum Hydrocarbon


Average wind speed


Non-dominated sorting genetic algorithm-II


Minimum wind speed


Modular three-dimensional transport model


Maximum wind speed


Initial chloride concentration


Gaussian process regression


Continuous genetic algorithm


Particle swarm optimization.


Differential evolution.


Receiver operating characteristics


Area under the ROC curve statistic


Fuzzy water quality index


True positive rate


Specific conductance


Water quality index


Single decision tree


Decision tree forest


Decision treeboost


Redox potential


Sum of squared errors


Self-organizing map


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The project was funded from UAE University within the initiatives of Asian Universities Alliance collaboration.

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Hanoon, M.S., Ahmed, A.N., Fai, C.M. et al. Application of Artificial Intelligence Models for modeling Water Quality in Groundwater: Comprehensive Review, Evaluation and Future Trends. Water Air Soil Pollut 232, 411 (2021).

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  • Groundwater quality (GWQ)
  • Artificial intelligence (AI)
  • Machine learning (ML)
  • ANN