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

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Abstract

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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Abbreviations

CO3:

Carbonate

NO2:

Nitrite

RBF:

Radial basis function

MLP:

Multi-layer perceptron

DL:

Deep learning

RF:

Random forest

XGBoost:

eXtreme gradient boosting

R2 :

Determination coefficient

RMSE:

Root mean squared error

MARE:

Mean absolute relative error

MLP:

Multilayer perceptron

GEP:

Gene expression programming.

SAR:

Sodium adsorption ratio

MAPE:

Mean absolute percentage error

SI:

Scatter index

R:

Correlation coefficient

MSE:

Mean square error

MAE:

Mean absolute error

FCM:

Fuzzy c-means

GP:

Grid partition

PSO:

Particle swarm optimization.

NF:

Neuro-fuzzy

SAR:

Sodium absorption ratio

BNs:

Bayesian networks

MTEs:

Mixtures of Truncated Exponentials

XGB:

Extreme gradient boosting

VAF:

Variance account for

PAEE:

Percent Average Estimation Error

PS:

Potential Salinity

DENFIS:

Dynamic evolving neural-fuzzy inference system

ESP:

Exchangeable Sodium Percentage

SVR:

Support vector regression

GMDH:

Group method of data handling

T:

Temperature

RSC:

Residual Sodium Carbonate

RBIAS:

Relative Bias

SOM:

Self-organized map

ANEP:

Average Normalized Error for Parameter Estimates

LWPR:

Locally weighted projection regression

RVM:

Relevance vector machines

BNN:

Bayesian neural network

RE:

Reduction of error

IA:

Index of agreement

KSOFM:

Kohonen self-organizing features map

FGQI:

Fuzzy-GIS-based groundwater quality index

ASVR:

Active Set Support Vector Regression

MAR:

Magnesium Adsorption Ratio

PMRE:

percent mean relative error

GQI:

Groundwater quality index

MARS:

Multivariate adaptive regression spline

M5 Tree:

M5 Tree model

GA:

Genetic Algorithm

GEP:

Gene expression programming.

TOC:

Total organic carbon

NSE:

Nash-Sutcliffe efficiency

WHO:

World health organization

LMI:

Legates and McCabe index

SDR:

Standard deviation ratio

WI:

Willmott index of agreement

NE:

Normalized error

MLR:

Multiple linear regression

SEM:

Structural equation modeling

GIS:

Geographic information system

FCT:

Fuzzy Clustering Technique

ACOR:

Ant colony optimization for continuous domains

Mmce:

Mean misclassification error

AARE:

Average absolute relative error

GRNN:

generalized regression neural network

ASE:

average squared error

RSC:

Residual sodium carbonate

PSVM:

Probabilistic Support Vector Machine

MAR:

Magnesium adsorption ratio

KR:

Kellys ratio

BPNN:

Back-propagation neural network

DE:

Differential evolution.

GP:

Gaussian Process

RT:

Random tree

PBIAS:

Percent of bias.

PSVMs:

Probabilistic support vector machines

PNNs:

Probabilistic neural networks

DO:

Dissolved oxygen

TA:

Total alkalinity

PBIAS:

Percent of bias.

BOD:

Biological oxygen demand

LSSVM:

Least square support vector machine

COD:

Chemical oxygen demand

SOM:

Self-organizing map

FFNN:

Feed forward neural network

FNN-SVR:

Fuzzy neural network-based support vector regression

CE:

Coefficient of efficiency

AIC:

Akaike information criterion

KNN:

K-nearest neighbor

WNN:

Wavelet neural network

MFIS:

Mamdani Fuzzy Inference System

As:

Arsenic

ELM:

Extreme learning machine

MLP:

Multi- layer perceptron

MABE:

Mean absolute bias error

PCR:

Principal component regression

BR:

Bayesian regulation

RR:

Recharge rate

A:

Abstraction

AVR:

Abstraction average rate

LT:

Lifetime

GWL:

Groundwater level

AT:

Aquifer thickness

DSWS:

Depth from the surface to well screen

DSSL:

Distance from sea shoreline

TR:

Total rainfall

RH:

Relative humidity

Tmin:

Minimum temperature

GPR:

Boosted regression tree

Tmax:

Maximum temperature

Tavg:

Average temperature

TPH:

Total Petroleum Hydrocarbon

W:

Average wind speed

NSGA-II:

Non-dominated sorting genetic algorithm-II

Wmin:

Minimum wind speed

MT3D:

Modular three-dimensional transport model

Wmax:

Maximum wind speed

ICC:

Initial chloride concentration

GPR:

Gaussian process regression

CGA:

Continuous genetic algorithm

PSO:

Particle swarm optimization.

DE:

Differential evolution.

ROC:

Receiver operating characteristics

AUC:

Area under the ROC curve statistic

FWQI:

Fuzzy water quality index

TPR:

True positive rate

SC:

Specific conductance

WQI:

Water quality index

SDT:

Single decision tree

DTF:

Decision tree forest

DTB:

Decision treeboost

RP:

Redox potential

SSE:

Sum of squared errors

SOM:

Self-organizing map

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Acknowledgements

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

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Authors and Affiliations

  1. College of Technical Engineering, Islamic University, Najaf, Iraq

    Marwah Sattar Hanoon

  2. Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor Darul Ehsan, Malaysia

    Ali Najah Ahmed

  3. Discipline of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia

    Chow Ming Fai

  4. Department of Civil Engineering, College of Engineering, Qassim University, Unaizah, Saudi Arabia

    Ahmed H. Birima

  5. College of Science, Al Muthanna University, Samawah, AL-Muthanna, Iraq

    Marwah Sattar Hanoon & Arif Razzaq

  6. Civil and Environmental Eng. Dept., College of Engineering, United Arab Emirates University, Al Ain, P.O. Box 15551, United Arab Emirates

    Mohsen Sherif

  7. National Water and Energy Center, United Arab Emirates University, Al Ain, P.O. Box 15551, United Arab Emirates

    Mohsen Sherif & Ahmed Sefelnasr

  8. Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    Ahmed El-Shafie

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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). https://doi.org/10.1007/s11270-021-05311-z

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