Skip to main content

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

Water, Air, & Soil Pollution volume 232, Article number: 411 (2021) Cite this article

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.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

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

References

  • Aguilera, P. A., Fernández, A., Ropero, R. F., & Molina, L. (2013). Groundwater quality assessment using data clustering based on hybrid Bayesian networks. Stochastic Environmental Research and Risk Assessment, 27(2), 435–447. https://doi.org/10.1007/s00477-012-0676-8

    Article  Google Scholar 

  • Ahmed, A., Masrur, A., & Shah, S. M. A. (2017). Application of adaptive Neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River. Journal of King Saud University - Engineering Sciences, 29(3), 237–243. https://doi.org/10.1016/j.jksues.2015.02.001

    Article  Google Scholar 

  • Alagha, J. S., Said, M. A. M., & Mogheir, Y. (2014). Modeling of nitrate concentration in groundwater using artificial intelligence approach-a case study of Gaza coastal aquifer. Environmental Monitoring and Assessment, 186(1), 35–45. https://doi.org/10.1007/s10661-013-3353-6

    Article  CAS  Google Scholar 

  • Alizamir, M., & Sobhanardakani, S. (2016). Forecasting of heavy metals concentration in groundwater resources of Asadabad plain using artificial neural network approach. J Adv Environ Health Res, 4(42), 68–77.

    CAS  Google Scholar 

  • Alizamir, M., & Sobhanardakani, S. (2018). An artificial neural network - particle swarm optimization (ANN- PSO) approach to predict heavy metals contamination in groundwater resources. Jundishapur Journal of Health Sciences, 10(2). https://doi.org/10.5812/jjhs.67544

  • Almasri, M. N., & Kaluarachchi, J. J. (2005). Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. Environmental Modelling & Software, 20(7), 851–871.

    Article  Google Scholar 

  • Arabgol, R., Sartaj, M., & Asghari, K. (2016). Predicting nitrate concentration and its spatial distribution in groundwater resources using support vector machines (SVMs) model. Environmental Modeling and Assessment, 21(1), 71–82. https://doi.org/10.1007/s10666-015-9468-0

    Article  Google Scholar 

  • Aryafar, Ahmad, Vahid Khosravi, Hosniyeh Zarepourfard, and Reza Rooki. 2019. “Evolving genetic programming and other AI-based models for estimating groundwater quality parameters of the Khezri plain, eastern Iran.” Environmental Earth Sciences 78(3):0. doi: https://doi.org/10.1007/s12665-019-8092-8.

  • Bain, R. E. S., Wright, J. A., Christenson, E., & Bartram, J. K. (2014). Rural:Urban inequalities in post 2015 targets and indicators for drinking-water. Science of the Total Environment, 490, 509–513. https://doi.org/10.1016/j.scitotenv.2014.05.007

    Article  CAS  Google Scholar 

  • Barzegar, R., & Moghaddam, A. A. (2016). Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction. Modeling Earth Systems and Environment, 2(1), 1–13. https://doi.org/10.1007/s40808-015-0072-8

    Article  Google Scholar 

  • Barzegar, R., Moghaddam, A. A., Adamowski, J., & Fijani, E. (2017). Comparison of machine learning models for predicting fluoride contamination in groundwater. Stochastic Environmental Research and Risk Assessment, 31(10), 2705–2718. https://doi.org/10.1007/s00477-016-1338-z

    Article  Google Scholar 

  • Bashi-Azghadi, S. N., Kerachian, R., Bazargan-Lari, M. R., & Solouki, K. (2010). Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN. Expert Systems with Applications, 37(10), 7154–7161. https://doi.org/10.1016/j.eswa.2010.04.019

    Article  Google Scholar 

  • Batayneh, A., Zaman, H., Zumlot, T., Ghrefat, H., Mogren, S., Nazzal, Y., Elawadi, E., Qaisy, S., Bahkaly, I., Al-taani, A., Batayneh, A., Zaman, H., Zumloť, T., Ghrefať, H., and Mogren, S.. (2016). “Hydrochemical Facies and ionic ratios of the coastal groundwater aquifer of Saudi Gulf of Aqaba : Implication for seawater intrusion stable URL : http://www.Jstor.Org/Stable/43289862 linked references are available on JSTOR for this article : Hydrochemica.”

  • Bedi, S., Samal, A., Ray, C., & Snow, D. (2020). Comparative evaluation of machine learning models for groundwater quality assessment. Environmental Monitoring and Assessment, 192(12). https://doi.org/10.1007/s10661-020-08695-3

  • Belkhiri, L., Mouni, L., Tiri, A., Narany, T. S., & Nouibet, R. (2018). Spatial analysis of groundwater quality using self-organizing maps. Groundwater for Sustainable Development, 7, 121–132.

    Article  Google Scholar 

  • Bhagat, S. K., Tiyasha, T., Tung, T. M., Mostafa, R. R., & Yaseen, Z. M. (2020). Manganese (Mn) removal prediction using extreme gradient model. Ecotoxicology and Environmental Safety, 204, 111059. https://doi.org/10.1016/j.ecoenv.2020.111059

    Article  CAS  Google Scholar 

  • Bilali, E., Ali, A. T., & Brouziyne, Y. (2021). Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agricultural Water Management, 245, 106625. https://doi.org/10.1016/j.agwat.2020.106625

    Article  Google Scholar 

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  • Bui, D. T., Khosravi, K., Karimi, M., Busico, G., Khozani, Z. S., Nguyen, H., Mastrocicco, M., Tedesco, D., Cuoco, E., & Kazakis, N. (2020). Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm. Science of the Total Environment, 715, 136836. https://doi.org/10.1016/j.scitotenv.2020.136836

    Article  CAS  Google Scholar 

  • Chen, T. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2939672.2939785

  • Chen, S.-T., & Pao-Shan, Y. (2007). Pruning of support vector networks on flood forecasting. Journal of Hydrology, 347(1), 67–78. https://doi.org/10.1016/j.jhydrol.2007.08.029

    Article  Google Scholar 

  • Cho, K. H., Sthiannopkao, S., Pachepsky, Y. A., Kim, K.-W., & Kim, J. H. (2011). Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network. Water Research, 45(17), 5535–5544. https://doi.org/10.1016/j.watres.2011.08.010

    Article  CAS  Google Scholar 

  • Choi, B.-Y., Yun, S.-T., Kim, K.-H., Kim, J.-W., Kim, H. M., & Koh, Y.-K. (2014). Hydrogeochemical interpretation of south Korean groundwater monitoring data using self-organizing maps. Journal of Geochemical Exploration, 137, 73–84. https://doi.org/10.1016/j.gexplo.2013.12.001

    Article  CAS  Google Scholar 

  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018

    Article  Google Scholar 

  • Dahiya, S., Singh, B., Shalini, G., Garg, V. K., & Kushwaha, H. S. (2007). Analysis of groundwater quality using fuzzy synthetic evaluation. Journal of Hazardous Materials, 147(3), 938–946. https://doi.org/10.1016/j.jhazmat.2007.01.119

    Article  CAS  Google Scholar 

  • Dar, I. A., Sankar, K., Dar, M. A., & Majumder, M. (2012). Fluoride contamination – Artificial neural network modeling and inverse distance weighting approach. Revue Des Sciences de l’Eau, 25(2), 165–182. https://doi.org/10.7202/1011606ar

    Article  CAS  Google Scholar 

  • Dixon, B. (2005). Groundwater vulnerability mapping: A GIS and fuzzy rule based integrated tool. Applied Geography, 25(4), 327–347. https://doi.org/10.1016/j.apgeog.2005.07.002

    Article  Google Scholar 

  • Dixon, B. (2009). A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N. Hydrogeology Journal, 17(6), 1507–1520. https://doi.org/10.1007/s10040-009-0451-1

    Article  CAS  Google Scholar 

  • Dohare, D., Deshpande, S., Kotiya, A.. (2014). “Analysis of Ground Water Quality Parameters: A Review Www.Isca.Me.” Research Journal of Engineering Sciences ___________________________________________ ISSN Res. J. Engineering Sci 3(5):2278–9472.

  • Eberhat, R., and Kennedy, J. (1995). “A new optimizer using particle swarm theory.” In Sixth international symposium on micro machine and human science, (pp. 39–43). Piscataway.

  • Ehteshami, M., Dolatabadi Farahani, N., & Tavassoli, S. (2016). Simulation of nitrate contamination in groundwater using artificial neural networks. Modeling Earth Systems and Environment, 2(1), 1–10. https://doi.org/10.1007/s40808-016-0080-3

    Article  Google Scholar 

  • Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x

    Article  CAS  Google Scholar 

  • Erickson, M. L., Elliott, S. M., Christenson, C. A., & Krall, A. L. (2018). Predicting Geogenic arsenic in drinking water Wells in glacial aquifers, north-Central USA: Accounting for depth-dependent features. Water Resources Research, 54(12), 10,172–10,187. https://doi.org/10.1029/2018WR023106

    Article  CAS  Google Scholar 

  • Esau, I., Miles, V., Varentsov, M., Konstantinov, P., & Melnikov, V. (2019). Spatial structure and temporal variability of a surface urban Heat Island in cold continental climate. Theoretical and Applied Climatology, 137(3), 2513–2528.

    Article  Google Scholar 

  • Esmaeilbeiki, F., Nikpour, M. R., Singh, V. K., Kisi, O., Sihag, P., & Sanikhani, H. (2020). Exploring the application of soft computing techniques for spatial evaluation of groundwater quality variables. Journal of Cleaner Production, 276, 124206. https://doi.org/10.1016/j.jclepro.2020.124206

    Article  CAS  Google Scholar 

  • Etemad-Shahidi, A., & Bonakdar, L. (2009). Design of rubble-mound breakwaters using M5 ′ machine learning method. Applied Ocean research, 31(3), 197–201. https://doi.org/10.1016/j.apor.2009.08.003

    Article  Google Scholar 

  • Fadipe, O. O., L. K. Abidoye, J. O. Adeosun, B. B. Oguntola, and O. Adewusi. (2021) “Simulation of groundwater quality characteristics using artificial neural network.” Nigerian Journal OfTechnology 40(1). https://doi.org/10.4314/njt.v40i1.21.

  • Fahimi, F., Yaseen, Z. M., & El-shafie, A. (2017). Application of soft computing based hybrid models in hydrological variables modeling: A comprehensive review. Theoretical and Applied Climatology, 128(3–4), 875–903. https://doi.org/10.1007/s00704-016-1735-8

    Article  Google Scholar 

  • Fijani, E., Nadiri, A. A., Moghaddam, A. A., Tsai, F. T. C., & Dixon, B. (2013). Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran. Journal of Hydrology, 503, 89–100. https://doi.org/10.1016/j.jhydrol.2013.08.038

    Article  CAS  Google Scholar 

  • Fijani, E., Moghaddam, A. A., Tsai, F. T. C., & Tayfur, G. (2017). Analysis and assessment of Hydrochemical characteristics of Maragheh-Bonab plain aquifer, northwest of Iran. Water Resources Management, 31(3), 765–780. https://doi.org/10.1007/s11269-016-1390-y

    Article  Google Scholar 

  • Fox, D. G. (1981). Judging air quality model performance. A summary of the AMS workshop on dispersion model performance, woods hole, Mass., 8-11 September 1980. Bulletin of the American Meteorological Society, 62(5), 599–609. https://doi.org/10.1175/1520-0477(1981)062<0599:JAQMP>2.0.CO;2

    Article  Google Scholar 

  • Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67. https://doi.org/10.1214/aos/1176347963

    Article  Google Scholar 

  • Gemitzi, A., Petalas, C., Pisinaras, V., & Tsihrintzis, V. A. (2010). Spatial prediction of nitrate pollution in Groundwaters using neural networks and GIS: An application to south Rhodope aquifer (Thrace, Greece). Hydrological Processes, 2274(November 2008). https://doi.org/10.1002/hyp

  • Gholami, R., Kamkar-Rouhani, A., Doulati Ardejani, F., & Maleki, S. (2011). Prediction of toxic metals concentration using artificial intelligence techniques. Applied Water Science, 1(3), 125–134. https://doi.org/10.1007/s13201-011-0016-z

    Article  CAS  Google Scholar 

  • Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99. https://doi.org/10.1023/A:1022602019183

    Article  Google Scholar 

  • Hadian, M., Saryazdi, S.M.E., Mohammadzadeh, A., and Babaei, M.. (2021). “Chapter 11 - Application of artificial intelligence in modeling, control, and fault diagnosis.” In Applications of Artificial Intelligence in Process Systems Engineering.(pp. 255–323). Elsevier.

  • Heidarzadeh, N. (2017). A practical low-cost model for prediction of the groundwater quality using artificial neural networks. Journal of Water Supply: Research and Technology - AQUA, 66(2), 86–95. https://doi.org/10.2166/aqua.2017.035

    Article  Google Scholar 

  • Hem, J.D.. (1985). Study and interpretation of the chemical characteristics of natural water. Vol. 2254. Department of the Interior, US Geological Survey.

  • Isazadeh, M., Biazar, S. M., & Ashrafzadeh, A. (2017). Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters. Environmental Earth Sciences, 76(17). https://doi.org/10.1007/s12665-017-6938-5

  • Jafari, R., Hassani, A. H., Torabian, A., Ghorbani, M. A., & Mirbagheri, S. A. (2019). Prediction of groundwater quality parameter in the Tabriz plain, Iran using soft computing methods. Journal of Water Supply: Research and Technology - AQUA, 68(7), 573–584. https://doi.org/10.2166/aqua.2019.062

    Article  Google Scholar 

  • Jalalkamali, A. (2015). Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters. Earth Science Informatics, 8(4), 885–894. https://doi.org/10.1007/s12145-015-0222-6

    Article  Google Scholar 

  • Jang, J. S. R. 1993. “ANFIS : Adap Tive-ne Twork-based fuzzy inference system.” IEEE Transactions on systems, 23(3).

  • Jang, J.S.R. (1996). Neuro fuzzy modeling for dynamic system identification. In Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium (pp. 320–25).

  • Jang, J.-S. R., Sun, C.-T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [book review]. IEEE Transactions on Automatic Control, 42(10), 1482–1484.

    Article  Google Scholar 

  • Jebastina, N., & Prince Arulraj, G. (2018). Spatial prediction of nitrate concentration using GIS and ANFIS Modelling in groundwater. Bulletin of Environmental Contamination and Toxicology, 101(3), 403–409. https://doi.org/10.1007/s00128-018-2406-5

    Article  CAS  Google Scholar 

  • Jha, M. K., Shekhar, A., & Annie Jenifer, M. (2020). Assessing groundwater quality for drinking water supply using hybrid fuzzy-GIS-based water quality index. Water Research, 179, 115867. https://doi.org/10.1016/j.watres.2020.115867

    Article  CAS  Google Scholar 

  • Jiang, Y., Wu, Y., Groves, C., Yuan, D., & Kambesis, P. (2009). Natural and anthropogenic factors affecting the groundwater quality in the Nandong karst Underground River system in Yunan, China. Journal of Contaminant Hydrology, 109(1), 49–61. https://doi.org/10.1016/j.jconhyd.2009.08.001

    Article  CAS  Google Scholar 

  • Kadam, A. K., Wagh, V. M., Muley, A. A., Umrikar, B. N., & Sankhua, R. N. (2019). Prediction of water quality index using artificial neural network and multiple linear regression Modelling approach in Shivganga River basin, India. Modeling Earth Systems and Environment, 5(3), 951–962. https://doi.org/10.1007/s40808-019-00581-3

    Article  Google Scholar 

  • Kassem, Y., Gökçekuş, H., & Maliha, M. R. M. (2021). Identifying Most influencing input parameters for predicting chloride concentration in groundwater using an ANN approach. Environmental Earth Sciences, 80(7), 248. https://doi.org/10.1007/s12665-021-09541-6

    Article  CAS  Google Scholar 

  • Keskin, T. E., Düğenci, M., & Kaçaroğlu, F. (2015). Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey). Environmental Earth Sciences, 73(9), 5333–5347. https://doi.org/10.1007/s12665-014-3784-6

    Article  Google Scholar 

  • Khaki, M., Yusoff, I., & Islami, N. (2015). Application of the artificial neural network and Neuro-fuzzy system for assessment of groundwater quality. Clean - Soil, Air, Water, 43(4), 551–560. https://doi.org/10.1002/clen.201400267

    Article  CAS  Google Scholar 

  • Khalil, A., Almasri, M. N., McKee, M., & Kaluarachchi, J. J. (2005). Applicability of statistical learning algorithms in groundwater quality modeling. Water Resources Research, 41(5), 1–16. https://doi.org/10.1029/2004WR003608

    Article  CAS  Google Scholar 

  • Khan, F.M., Gupta, R., and Sekhri, S. (2021). Superposition learning-based model for prediction of E.Coli in groundwater using physico-chemical water quality parameters. Groundwater for Sustainable Development 100580. doi: https://doi.org/10.1016/j.gsd.2021.100580.

  • Khudair, B. H., Jasim, M. M., & Alsaqqa, A. S. (2018). Artificial neural network model for the prediction of groundwater quality. International Journal of Plant & Soil Science, 8(4), 1–13. https://doi.org/10.9734/ijpss/2015/19686

    Article  Google Scholar 

  • Kisi, O., Keshavarzi, A., Shiri, J., Zounemat-Kermani, M., & Omran, E. S. E. (2017). Groundwater quality modeling using Neuro-particle swarm optimization and Neuro-differential evolution techniques. Hydrology Research, 48(6), 1508–1519. https://doi.org/10.2166/nh.2017.206

    Article  CAS  Google Scholar 

  • Kisi, O., Azad, A., Kashi, H., Saeedian, A., Hashemi, S. A. A., & Ghorbani, S. (2019). Modeling groundwater quality parameters using hybrid Neuro-fuzzy methods. Water Resources Management, 33(2), 847–861. https://doi.org/10.1007/s11269-018-2147-6

    Article  Google Scholar 

  • Klçaslan, Y., Tuna, G., Gezer, G., Gulez, K., Arkoc, O., and Potirakis, S. M.. (2014). ANN based estimation of groundwater quality using a wireless water quality network. International Journal of Distributed Sensor Networks 2014. doi: https://doi.org/10.1155/2014/458329.

  • Knierim, K. J., Kingsbury, J. A., Haugh, C. J., & Ransom, K. M. (2020). Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, Central United States. Journal of the American Water Resources Association, 56(6), 1010–1029. https://doi.org/10.1111/1752-1688.12879

    Article  Google Scholar 

  • Knoll, L., Breuer, L., & Bach, M. (2017). Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning. Science of the Total Environment, 668, 1317–1327. https://doi.org/10.1016/j.scitotenv.2019.03.045

    Article  CAS  Google Scholar 

  • Koza, J. R., Bennett, F. H., Andre, D., Keane, M. A., & Dunlap, F. (1997). Automated synthesis of analog electrical circuits by means of genetic programming. IEEE Transactions on Evolutionary Computation, 1(2), 109–128.

    Article  Google Scholar 

  • Krapivin, V. F., Varotsos, C. A., & Nghia, B. Q. (2017). A modeling system for monitoring water quality in lagoons. Water, Air, and Soil Pollution, 228(10). https://doi.org/10.1007/s11270-017-3581-4

  • Krapivin, V. F., Varotsos, C. A., & Marechek, S. V. (2018). The dependence of the soil microwave attenuation on frequency and water content in different types of vegetation: An empirical model. Water, Air, & Soil Pollution, 229(4), 110. https://doi.org/10.1007/s11270-018-3773-6

    Article  CAS  Google Scholar 

  • Krapivin, V. F., Mkrtchan, F. A., Varotsos, C. A., & Xue, Y. (2021). Operational diagnosis of arctic waters with instrumental technology and information modeling. Water, Air, & Soil Pollution, 232(4), 137. https://doi.org/10.1007/s11270-021-05068-5

    Article  CAS  Google Scholar 

  • Kumar, P. G. D., Viswanath, N. C., Cyrus, S., & Abraham, B. M. (2020). Mixing data for multivariate statistical study of groundwater quality. Environmental Monitoring and Assessment, 192(8). https://doi.org/10.1007/s10661-020-08465-1

  • Kuo, Y. M., Liu, C. W., & Lin, K. H. (2004). Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of Blackfoot disease in Taiwan. Water Research, 38(1), 148–158. https://doi.org/10.1016/j.watres.2003.09.026

    Article  CAS  Google Scholar 

  • Lindskog, P. (1997). Fuzzy identification from a grey box modeling point of view. In Fuzzy model identification (pp. 3–50). Springer.

  • Liu, B., Wang, L., & Jin, Y. (2007). An effective PSO-based Memetic algorithm for flow shop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37(1), 18–27. https://doi.org/10.1109/TSMCB.2006.883272

    Article  Google Scholar 

  • Lopez, A. M., Wells, A., & Fendorf, S. (2021). Soil and aquifer properties combine as predictors of groundwater uranium concentrations within the Central Valley, California. Environmental Science & Technology, 55(1), 352–361. https://doi.org/10.1021/acs.est.0c05591

    Article  CAS  Google Scholar 

  • Lowe, D., Broomhead, D., and Malvern (United Kingdom); Royal Signals and Radar Establishment. 1988. Radial basis functions multivariable functional interpolation and adaptive networks. No. RSRE-MEMO-4148. Royal signals and radar establishment Malvern (United Kingdom).

  • Maiti, S., Erram, V. C., Gupta, G., Tiwari, R. K., Kulkarni, U. D., & Sangpal, R. R. (2013). Assessment of groundwater quality: A fusion of geochemical and geophysical information via Bayesian neural networks. Environmental Monitoring and Assessment, 185(4), 3445–3465. https://doi.org/10.1007/s10661-012-2802-y

    Article  Google Scholar 

  • Maroufpoor, S., Jalali, M., Nikmehr, S., Shiri, N., Shiri, J., and Maroufpoor, E.. (2020). “Modeling groundwater quality by using hybrid intelligent and Geostatistical methods.” Environmental Science and Pollution Research, 27, 28183–97

  • Mehr, D., Ali, V. N., Kahya, E., Hrnjica, B., Sattar, A. M. A., & Yaseen, Z. M. (2018). Genetic programming in water resources engineering: A state-of-the-art review. Journal of Hydrology, 566, 643–667. https://doi.org/10.1016/j.jhydrol.2018.09.043

    Article  Google Scholar 

  • Melin, P., Olivas, F., Castillo, O., Valdez, F., Soria, J., & Valdez, M. (2013). Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Systems with Applications, 40(8), 3196–3206. https://doi.org/10.1016/j.eswa.2012.12.033

    Article  Google Scholar 

  • Moasheri, S. A., & Tabatabaie, S. M. (2013). Estimate the spatial distribution TDS the fusion method geostatistics and artificial neural networks. International Journal of Agriculture, 3(4), 699.

    Google Scholar 

  • Moasheri, S.A., Khammar, G., Poornoori, Z., and Beyranvand, Z.. (2013). “Estimate the Spatial Distribution TDS the Fusion Method Geostatistics and Artificial Neural Networks.” International Journal of Agriculture and Crop Sciences. 410–20.

  • Mohammadi, S., Amir, A. S. S., & Naseri, A. A. (2017). Simulation of groundwater quality parameters using ANN and ANN+PSO models (case study: Ramhormoz plain). Pollution, 3(2), 191–200. https://doi.org/10.7508/pj.2017.02.003

    Article  Google Scholar 

  • Moosavi, V., Vafakhah, M., Shirmohammadi, B., & Ranjbar, M. (2012). Optimization of wavelet-ANFIS and wavelet-ANN hybrid models by Taguchi method for groundwater level forecasting. Arabian Journal for Science and Engineering, 39(3), 1785–1796. https://doi.org/10.1007/s13369-013-0762-3

    Article  Google Scholar 

  • Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Daren Harmel, R., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900.

    Article  Google Scholar 

  • Nadiri, A. A., Fijani, E., Tsai, F. T. C., & Moghaddam, A. A. (2013). Supervised committee machine with artificial intelligence for prediction of fluoride concentration. Journal of Hydroinformatics, 15(4), 1474–1490. https://doi.org/10.2166/hydro.2013.008

    Article  CAS  Google Scholar 

  • Nadiri, A. A., Sedghi, Z., Khatibi, R., & Gharekhani, M. (2017). Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures. Science of the Total Environment, 593–594, 75–90. https://doi.org/10.1016/j.scitotenv.2017.03.109

    Article  CAS  Google Scholar 

  • Nadiri, A. A., Norouzi, H., Khatibi, R., & Gharekhani, M. (2019). Groundwater DRASTIC vulnerability mapping by unsupervised and supervised techniques using a Modelling strategy in two levels. Journal of Hydrology, 574, 744–759. https://doi.org/10.1016/j.jhydrol.2019.04.039

    Article  Google Scholar 

  • Nakagawa, K., Amano, H., Kawamura, A., & Berndtsson, R. (2017). Classification of groundwater chemistry in Shimabara, using self-organizing maps. Hydrology Research, 48(3), 840–850. https://doi.org/10.2166/nh.2016.072

    Article  CAS  Google Scholar 

  • Narany, S., Tahoora, M. F. R., Aris, A. Z., Sulaiman, W. N. A., Juahir, H., & Fakharian, K. (2014). Identification of the hydrogeochemical processes in groundwater using classic integrated geochemical methods and geostatistical techniques, in Amol-Babol Plain, Iran. The Scientific World Journal. https://doi.org/10.1155/2014/419058

  • Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  • Nasr, M., & Zahran, H. F. (2014). Using of PH as a tool to predict salinity of groundwater for irrigation purpose using artificial neural network. The Egyptian Journal of Aquatic Research, 40(2), 111–115. https://doi.org/10.1016/j.ejar.2014.06.005

    Article  Google Scholar 

  • Nikoo, M. R., & Mahjouri, N. (2013). Water quality zoning using probabilistic support vector machines and self-organizing maps. Water Resources Management, 27(7), 2577–2594. https://doi.org/10.1007/s11269-013-0304-5

    Article  Google Scholar 

  • Nolan, B. T., Fienen, M. N., & Lorenz, D. L. (2015). A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA. Journal of Hydrology, 531, 902–911. https://doi.org/10.1016/j.jhydrol.2015.10.025

    Article  CAS  Google Scholar 

  • Norouzi, H., & Moghaddam, A. A. (2020). Groundwater quality assessment using random Forest method based on groundwater quality indices (case study: Miandoab plain aquifer, NW of Iran). Arabian Journal of Geosciences, 13(18). https://doi.org/10.1007/s12517-020-05904-8

  • Prajapati, P., and Parekh, F.. 2016. Analysis of groundwater quality parameters using Mamdani fuzzy inference system (MFIS). International Journal of Science and Research (IJSR) 5(1), 1335–40. doi: https://doi.org/10.21275/v5i1.nov153010.

  • Prasad, B., Kumari, P., Bano, S., & Kumari, S. (2014). Ground water quality evaluation near mining area and development of heavy metal pollution index. Applied Water Science, 4(1), 11–17. https://doi.org/10.1007/s13201-013-0126-x

    Article  CAS  Google Scholar 

  • Prasanna, M. V., Chidambaram, S., Shahul Hameed, A., & Srnivasamoorthy, K. (2011). Hydrogeochemical analysis and evaluation of groundwater quality in the Gadilam River basin, Tamil Nadu, India. Journal of Earth System Science, 120(1), 85–98. https://doi.org/10.1007/s12040-011-0004-6

    Article  CAS  Google Scholar 

  • Purkait, B., Kadam, S. S., & Das, S. K. (2008). Application of artificial neural network model to study arsenic contamination in groundwater of Malda District , Eastern India. ISEIS Journal of Environmental Informatics, 12(2), 140–149. https://doi.org/10.3808/jei.200800132

    Article  Google Scholar 

  • RadFard, M., Seif, M., Hashemi, A. H. G., Zarei, A., Saghi, M. H., Shalyari, N., Morovati, R., Heidarinejad, Z., & Samaei, M. R. (2019). Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and arc-Gis. MethodsX, 6, 1021–1029.

    Article  Google Scholar 

  • Raghavendra, N., Sujay, & Deka, P. C. (2014). Support vector machine applications in the field of hydrology: A review. Applied Soft Computing, 19, 372–386. https://doi.org/10.1016/j.asoc.2014.02.002

    Article  Google Scholar 

  • Rahmati, O., Choubin, B., Fathabadi, A., Coulon, F., Soltani, E., Shahabi, H., Mollaefar, E., Tiefenbacher, J., Cipullo, S., Ahmad, B. B., & Bui, D. T. (2019). Predicting uncertainty of machine learning models for Modelling nitrate pollution of groundwater using Quantile regression and UNEEC methods. Science of the Total Environment, 688, 855–866. https://doi.org/10.1016/j.scitotenv.2019.06.320

    Article  CAS  Google Scholar 

  • Rajaee, T., Ebrahimi, H., & Nourani, V. (2019). A review of the artificial intelligence methods in groundwater level modeling. Journal of Hydrology, 572, 336–351. https://doi.org/10.1016/j.jhydrol.2018.12.037

    Article  Google Scholar 

  • Ransom, K. M., Nolan, B. T., Traum, J. A., Faunt, C. C., Bell, A. M., Gronberg, J. A. M., Wheeler, D. C., Rosecrans, C. Z., Jurgens, B., Schwarz, G. E., Belitz, K., Eberts, S. M., Kourakos, G., & Harter, T. (2017). A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA. Science of the Total Environment, 601–602, 1160–1172. https://doi.org/10.1016/j.scitotenv.2017.05.192

    Article  CAS  Google Scholar 

  • Rodriguez-Galiano, V., Mendes, M. P., Garcia-Soldado, M. J., Chica-Olmo, M., & Ribeiro, L. (2014). Predictive modeling of groundwater nitrate pollution using random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (southern Spain). Science of the Total Environment, 476–477, 189–206. https://doi.org/10.1016/j.scitotenv.2014.01.001

    Article  CAS  Google Scholar 

  • Rodriguez-Galiano, V. F., Luque-Espinar, J. A., Chica-Olmo, M., & Mendes, M. P. (2018). Feature selection approaches for predictive Modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods. Science of the Total Environment, 624, 661–672.

    Article  CAS  Google Scholar 

  • Sahoo, G. B., Ray, C., Mehnert, E., & Keefer, D. A. (2006). Application of artificial neural networks to assess pesticide contamination in shallow groundwater. Science of the Total Environment, 367(1), 234–251. https://doi.org/10.1016/j.scitotenv.2005.12.011

    Article  CAS  Google Scholar 

  • Sakizadeh, M. (2015). Artificial intelligence for the prediction of water quality index in groundwater systems. Modeling Earth Systems and Environment, 2(1), 8. https://doi.org/10.1007/s40808-015-0063-9

    Article  Google Scholar 

  • Sasikaran, S., Sritharan, K., Balakumar, S., & Arasaratnam, V. (2016). Physical, chemical and microbial analysis of bottled drinking water. Ceylon Medical Journal. https://doi.org/10.4038/cmj.v57i3.4149

  • Selakov, A., Cvijetinović, D., Milović, L., Mellon, S., & Bekut, D. (2014). Hybrid PSO–SVM method for short-term load forecasting during periods with significant temperature variations in City of Burbank. Applied Soft Computing, 16, 80–88. https://doi.org/10.1016/j.asoc.2013.12.001

    Article  Google Scholar 

  • Selvaraj, A., Saravanan, S., & Jennifer, J. J. (2020). Mamdani fuzzy based decision support system for prediction of groundwater quality: An application of soft computing in water resources. Environmental Science and Pollution Research, 27(20), 25535–25552. https://doi.org/10.1007/s11356-020-08803-3

    Article  CAS  Google Scholar 

  • Singh, R. M., & Datta, B. (2004). Groundwater pollution source identification and simultaneous parameter estimation using pattern matching by artificial neural network. Environmental Forensics, 5(3), 143–153. https://doi.org/10.1080/15275920490495873

    Article  CAS  Google Scholar 

  • Singh, K. P., Gupta, S., & Mohan, D. (2014). Evaluating influences of seasonal variations and anthropogenic activities on alluvial groundwater hydrochemistry using ensemble learning approaches. Journal of Hydrology, 511, 254–266. https://doi.org/10.1016/j.jhydrol.2014.01.004

    Article  CAS  Google Scholar 

  • Singha, S., Pasupuleti, S., Singha, S. S., & Kumar, S. (2020). Effectiveness of groundwater heavy metal pollution indices studies by deep-learning. Journal of Contaminant Hydrology, 235, 103718. https://doi.org/10.1016/j.jconhyd.2020.103718

    Article  CAS  Google Scholar 

  • Singha, S., Pasupuleti, S., Singha, S. S., Singh, R., & Kumar, S. (2021). Prediction of groundwater quality using efficient machine learning technique. Chemosphere, 130265. https://doi.org/10.1016/j.chemosphere.2021.130265

  • Sirat, M. (2012). Neural network assessment of groundwater contamination of US mid-continent. Arabian Journal of Geosciences, 6(8), 3149–3160. https://doi.org/10.1007/s12517-012-0570-1

    Article  CAS  Google Scholar 

  • Smith, D. G. (1990). A better water quality indexing system for Rivers and streams. Water Research, 24(10), 1237–1244. https://doi.org/10.1016/0043-1354(90)90047-A

    Article  CAS  Google Scholar 

  • Sobhanardakani, S.. (2016). Evaluation of the water quality pollution indices for groundwater resources of Ghahavand Plain, Hamadan Province, Western Iran. Iranian Jornal of Toxicology 10 (3), 35–40. doi: https://doi.org/10.29252/arakmu.10.3.35.

  • Srinivas, R., Prashant Bhakar, and Ajit Pratap Singh. 2015. Groundwater quality assessment in some selected area of Rajasthan, India using fuzzy multi-criteria decision making tool. Aquatic Procedia 4(Icwrcoe):1023–30. doi: https://doi.org/10.1016/j.aqpro.2015.02.129.

  • Srinivasamoorthy, K., Vasanthavigar, M., Vijayaraghavan, K., Sarathidasan, R., & Gopinath, S. (2013). Hydrochemistry of groundwater in a coastal region of Cuddalore District, Tamilnadu, India: Implication for quality assessment. Arabian Journal of Geosciences, 6(2), 441–454. https://doi.org/10.1007/s12517-011-0351-2

    Article  CAS  Google Scholar 

  • Stackelberg, P. E., Belitz, K., Brown, C. J., Erickson, M. L., Elliott, S. M., Kauffman, L. J., Ransom, K. M., & Reddy, J. E. (2021). Machine learning predictions of PH in the glacial aquifer system, northern USA. Groundwater, 1–17. https://doi.org/10.1111/gwat.13063

  • Sunayana, K. K., Dube, O., & Sharma, R. (2020). Use of neural networks and spatial interpolation to predict groundwater quality. Environment, Development and Sustainability, 22(4), 2801–2816. https://doi.org/10.1007/s10668-019-00319-2

    Article  Google Scholar 

  • Suykens, J. A. K., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300.

    Article  Google Scholar 

  • Swarna Latha, P., & Nageswara Rao, K. (2012). An integrated approach to assess the quality of groundwater in a coastal aquifer of Andhra Pradesh, India. Environmental Earth Sciences, 66(8), 2143–2169. https://doi.org/10.1007/s12665-011-1438-5

    Article  CAS  Google Scholar 

  • Tabach, E., Eddy, L. L., Shahrour, I., & Najjar, Y. (2007). Use of artificial neural network simulation Metamodelling to assess groundwater contamination in a road project. Mathematical and Computer Modelling, 45(7), 766–776. https://doi.org/10.1016/j.mcm.2006.07.020

    Article  Google Scholar 

  • Tiyasha, T. M. T., & Yaseen, Z. M. (2020). A survey on river water quality Modelling using artificial intelligence models: 2000–2020. Journal of Hydrology, 585, 124670. https://doi.org/10.1016/j.jhydrol.2020.124670

    Article  CAS  Google Scholar 

  • Tutmez, B., Hatipoglu, Z., & Kaymak, U. (2006). Modelling electrical conductivity of groundwater using an adaptive Neuro-fuzzy inference system. Computers and Geosciences, 32(4), 421–433. https://doi.org/10.1016/j.cageo.2005.07.003

    Article  CAS  Google Scholar 

  • Vadiati, M., Asghari-Moghaddam, A., Nakhaei, M., Adamowski, J., & Akbarzadeh, A. H. (2016). A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices. Journal of Environmental Management, 184, 255–270. https://doi.org/10.1016/j.jenvman.2016.09.082

    Article  CAS  Google Scholar 

  • Varotsos, C. A., & Krapivin, V. F. (2018). Arctic waters has reached a critical point: An innovative approach to this problem. Water, Air, & Soil Pollution, 229(11), 343. https://doi.org/10.1007/s11270-018-4004-x

    Article  CAS  Google Scholar 

  • Varotsos, C. A., Krapivin, V. F., & Mkrtchyan, F. A. (2019). New optical tools for water quality diagnostics. Water, Air, and Soil Pollution, 230(8). https://doi.org/10.1007/s11270-019-4228-4

  • Varotsos, C.A., Krapivin, V.F., and Mkrtchyan, F.A.. (2020a). On the recovery of the water balance. Water, Air, & Soil Pollution 231(4):170. doi: https://doi.org/10.1007/s11270-020-04554-6.

  • Varotsos, C. A., Krapivin, V. F., Mkrtchyan, F. A., Gevorkyan, S. A., & Cui, T. (2020b). A novel approach to monitoring the quality of lakes water by optical and modeling tools: Lake Sevan as a case study. Water, Air, and Soil Pollution, 231(8). https://doi.org/10.1007/s11270-020-04792-8

  • Varotsos, C. A., Krapivin, V. F., Mkrtchyan, F. A., & Xue, Y. (2021). Optical spectral tools for diagnosing water media quality: A case study on the Angara/Yenisey River system in the Siberian region. Land, 10(4).

  • Vijay, S., & Kamaraj, K. (2019). Ground water quality prediction using machine learning algorithms in R. International Journal of Research and Analytical Reviews, 6(1), 743–749.

    Google Scholar 

  • Wagh, V. M., Panaskar, D. B., Muley, A. A., Mukate, S. V., Lolage, Y. P., & Aamalawar, M. L. (2016). Prediction of groundwater suitability for irrigation using artificial neural network model: A case study of Nanded tehsil, Maharashtra, India. Modeling Earth Systems and Environment, 2(4), 1–10. https://doi.org/10.1007/s40808-016-0250-3

    Article  Google Scholar 

  • Wagh, Vasant Madhav, Dipak Baburao Panaskar, and Aniket Avinash Muley. 2017. “Estimation of nitrate concentration in groundwater of Kadava River basin-Nashik District, Maharashtra, India by using artificial neural network model.” Modeling Earth Systems and Environment 3(1):0. doi: https://doi.org/10.1007/s40808-017-0290-3.

  • Wang, M. X., Liu, G. D., Wu, W. L., Bao, Y. H., & Liu, W. N. (2006). Prediction of agriculture derived groundwater nitrate distribution in North China plain with GIS-based BPNN. Environmental Geology, 50(5), 637–644. https://doi.org/10.1007/s00254-006-0237-x

    Article  CAS  Google Scholar 

  • Yang, Q., Zhang, J., Hou, Z., Lei, X., Tai, W., Chen, W., & Chen, T. (2017). Shallow groundwater quality assessment: Use of the improved Nemerow pollution index, wavelet transform and neural networks. Journal of Hydroinformatics, 19(5), 784–794. https://doi.org/10.2166/hydro.2017.224

    Article  Google Scholar 

  • Yaseen, Z. M., Sulaiman, S. O., Deo, R. C., & Chau, K.-W. (2019). An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. Journal of Hydrology, 569, 387–408. https://doi.org/10.1016/j.jhydrol.2018.11.069

    Article  Google Scholar 

  • Yesilnacar, M. I., & Sahinkaya, E. (2012). Artificial neural network prediction of sulfate and SAR in an unconfined aquifer in southeastern Turkey. Environmental Earth Sciences, 67(4), 1111–1119.

    Article  CAS  Google Scholar 

  • Yoon-Seok Hong, Michael R. Rosen, and Institute. (2001). Intelligent characterisation and diagnosis of the groundwater quality in an urban fractured-rock aquifer using an artificial neural network. Urban Water 3, 53(4), 551–562. https://doi.org/10.1111/j.1467-8500.1994.tb01504.x

    Article  Google Scholar 

  • Zaqoot, H. A., Hamada, M., & Miqdad, S. (2018). A comparative study of Ann for predicting nitrate concentration in groundwater Wells in the southern area of Gaza strip. Applied Artificial Intelligence, 32(7–8), 727–744.

    Article  Google Scholar 

Download references

Acknowledgements

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

Author information

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

Authors
  1. Marwah Sattar Hanoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Najah Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chow Ming Fai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed H. Birima
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arif Razzaq
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohsen Sherif
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ahmed Sefelnasr
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ahmed El-Shafie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Najah Ahmed.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

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

Keywords

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