Skip to main content
SpringerLink
Log in

Groundwater quality modeling using a novel hybrid data-intelligence model based on gray wolf optimization algorithm and multi-layer perceptron artificial neural network: a case study in Asadabad Plain, Hamedan, Iran

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Monitoring and assessment of groundwater quality (GWQ) as an important freshwater source for drinking purposes in urban and rural regions of developing countries due to rapidly increasing contamination is one of the concerns of water managers. Therefore, developing an efficient intelligent model for analyzing GWQ could help hydro-environmental engineers for sustainable water supply. The current research investigated the applicability of a novel nature-inspired optimization algorithm hybridized with multi-layer perceptron artificial neural network based on gray wolf optimization (GWO) for estimating dissolved oxygen (DO) total dissolved solid (TDS) and turbidity parameters at Asadabad Plain, Iran, and results are compared with the stand-alone multi-layer perceptron artificial neural network (MLPANN), generalized regression neural network (GRNN), and multiple linear regression (MLR) approaches. Evaluation of performance of models is carried out using various statistical indices like relative root mean square error, Nash-Sutcliffe efficiency, and correlation coefficient. Based on the results obtained, it is found that the hybrid GWO-MLPANN is a beneficial GWQ forecasting tool in accordance to high performance accuracy. Also, the study found that the superiority of the applied meta-heuristic algorithm (GWO) in improving the performance accuracy of the stand-alone artificial intelligence techniques in modeling the GWQ parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Data availability

The authors declare that they have not need research data support with this submission. Also, the authors are sure that all data and materials as well as software application or custom code support their published claims and comply with field standards.

References

  • Aduojo AA, Adebowole AE, Okezie U (2020) Modeling groundwater total dissolved solid from derived electromagnetic data using multiple linear regression analysis: a case study of groundwater contamination. Model Earth Syst Environ 6(3):1863–1875

    Article  Google Scholar 

  • Ali M, Prasad R, Xiang Y, Deo RC (2020) Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms. Renew Sust Energ Rev 132:110003

    Article  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(2):68–77

    CAS  Google Scholar 

  • Alizamir M, Sobhanardakani S (2017a) A comparison of performance of artificial neural networks for prediction of heavy metals concentration in groundwater resources of Toyserkan Plain. Avicenna J Environ Health Eng 4(1):11792–11792

    Article  Google Scholar 

  • Alizamir M, Sobhanardakani S (2017b) Predicting arsenic and heavy metals contamination in groundwater resources of Ghahavand plain based on an artificial neural network optimized by imperialist competitive algorithm. Environ Health Eng Manage J 4(4):225–231

    Article  CAS  Google Scholar 

  • Alizamir M, Sobhanardakani S, Taghavi L (2017) Modeling of groundwater resources heavy metals concentration using soft computing methods: application of different types of artificial neural networks. J Chem Health Risk 7(3):207–216

    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 J Health Sci 10(2):e67544

    Article  Google Scholar 

  • Alizamir M, Sobhanardakani S, Hasanalipour Shahrabadi A (2019) Prediction of heavy metals concentration in the groundwater resources in Razan Plain: extreme learning machine vs. artificial neural network and multivariate adaptive regression spline. Ann Mil Health Sci Res 17(4):e98554

    Article  Google Scholar 

  • Alizamir M, Kim S, Kisi O, Zounemat-Kermani M (2020) A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: case studies of the USA and Turkey regions. Energy 197:117239

    Article  Google Scholar 

  • Alizamir M, Heddam S, Kim S, Mehr AD (2021) On the implementation of a novel data-intelligence model based on extreme learning machine optimized by bat algorithm for estimating daily chlorophyll-a concentration: case studies of river and lake in USA. J Clean Prod 285:124868

    Article  CAS  Google Scholar 

  • Alley WM, Healy RW, LaBaugh JW, Reilly TE (2002) Flow and storage in groundwater systems. Science 296(5575):1985–1990

    Article  CAS  Google Scholar 

  • Allouche N, Maanan M, Gontara M, Rollo N, Jmal I, Bouri S (2017) A global risk approach to assessing groundwater vulnerability. Environ Model Softw 88:168–182

    Article  Google Scholar 

  • Bedi S, Samal A, Ray C, Snow D (2020) Comparative evaluation of machine learning models for groundwater quality assessment. Environ Monit Assess 192(12):1–23

    Article  Google Scholar 

  • Bui DT, Khosravi K, Karimi M, Busico G, Khozani ZS, Nguyen H, Kazakis N (2020) Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm. Sci Total Environ 715:136836

    Article  CAS  Google Scholar 

  • El Bilali A, Taleb A, Brouziyne Y (2021) Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agr Water Manage 245:106625

    Article  Google Scholar 

  • Fazel Tavassol S, Gopalakrishna GS (2015) Qualitative classification of Asadabad Hamedan plain water resources for various application using hydrogeochemical analysis data. XIV Convention of Mineralogical Society of India and National seminar on recent advances in research on Precambrian terrains in India.

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Hakim AMY, Baja S, Rampisela DA, Arif S (2020) Modelling land use/land cover changes prediction using multi-layer perceptron neural network (MLPNN): a case study in Makassar City, Indonesia. Int J Environ Stud 78:301–318. https://doi.org/10.1080/00207233.2020.1804730

    Article  Google Scholar 

  • Haykin S (1998) Neural networks—a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River, pp 26–32

    Google Scholar 

  • Hong H, Tsangaratos P, Ilia I, Loupasakis C, Wang Y (2020) Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping. Sci Total Environ 742:140549

    Article  CAS  Google Scholar 

  • Jafari R, Torabian A, Ghorbani MA, Mirbagheri SA, Hassani AH (2019) Prediction of groundwater quality parameter in the Tabriz plain, Iran using soft computing methods. J Water Suppl Res Technol—AQUA 68(7):573–584

    Article  Google Scholar 

  • Jang WS, Engel B, Yeum CM (2020) Integrated environmental modeling for efficient aquifer vulnerability assessment using machine learning. Environ Model Softw 124:104602

    Article  Google Scholar 

  • Jha MK, Shekhar A, Jenifer MA (2020) Assessing groundwater quality for drinking water supply using hybrid fuzzy-GIS-based water quality index. Water Res 179:115867

    Article  CAS  Google Scholar 

  • Kadam AK, Wagh VM, Muley AA, Umrikar BN, Sankhua RN (2019) Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India. Model Earth Syst Environ 5:951–962

    Article  Google Scholar 

  • Kisi O, Alizamir M, Docheshmeh Gorgij A (2020) Dissolved oxygen prediction using a new ensemble method. Environ Sci Pollut Res 27:9589–9603

    Article  CAS  Google Scholar 

  • Kisi O, Azad A, Kashi H, Saeedian A, Hashemi SAA, Ghorbani S (2019) Modeling groundwater quality parameters using hybrid neuro-fuzzy methods. Water Res Manage 33(2):847–861

    Article  Google Scholar 

  • Lan T, Tong C, Yu H, Shi X, Luo L (2020) Nonlinear process monitoring based on decentralized generalized regression neural networks. Expert Syst Appl 150:113273

    Article  Google Scholar 

  • Maroufpoor S, Jalali M, Nikmehr S, Shiri N, Shiri J, Maroufpoor E (2020) Modeling groundwater quality by using hybrid intelligent and geostatistical methods. Environ Sci Pollut Res 27:28183–28197

    Article  CAS  Google Scholar 

  • Mukate S, Panaskar D, Wagh V, Muley A, Jangam C, Pawar R (2018) Impact of anthropogenic inputs on water quality in Chincholi industrial area of Solapur, Maharashtra, India. Groundw Sustain Dev 7:359–371

    Article  Google Scholar 

  • Pham QB, Mohammadpour R, Linh NTT, Mohajane M, Pourjasem A, Sammen SS, Anh DT (2021) Application of soft computing to predict water quality in wetland. Environ Sci Pollut Res 28(1):185–200

    Article  CAS  Google Scholar 

  • Rajaee T, Nourani V, Zounemat-Kermani M, Kisi O (2011) River suspended sediment load prediction: application of ANN and wavelet conjunction model. J Hydrol Eng 16(8):613–627

    Article  Google Scholar 

  • Shah MI, Javed MF, Abunama T (2021) Proposed formulation of surface water quality and modelling using gene expression, machine learning, and regression techniques. Environ Sci Pollut Res 28(11):13202–13220

    Article  CAS  Google Scholar 

  • Snedecor GW, Cochran WG (1981) Statistical methods, 7th edn. Iowa State Univ. Press, Ames

    Google Scholar 

  • Specht DF (1991) A general regression neural network. IEEE T Neural Netw 2(6):568–576

    Article  CAS  Google Scholar 

  • Sobhanardakani S (2016) Evaluation of the water quality pollution indices for groundwater resources of Ghahavand plain, Hamadan province, western Iran. Iran J Toxicol 10(3):35–40

    Article  CAS  Google Scholar 

  • Sobhanardakani S, Yari AR, Taghavi L, Tayebi L (2016) Water quality pollution indices to assess the heavy metal contamination, case study: groundwater resources of Asadabad Plain in 2012. Arch Hyg Sci 5(4):221–228

    CAS  Google Scholar 

  • Sobhanardakani S, Taghavi L, Shahmoradi B, Jahangard A (2017) Groundwater quality assessment using the water quality Pollution indices in Toyserkan Plain. Environ Health Eng Manage J 4(1):21–27

    Article  CAS  Google Scholar 

  • Solley WB, Pierce RR, Perlman HA (1998) Estimated use of water in the United States in 1995. US Geol Surv

  • Vijay S, Kamaraj K (2021) Prediction of water quality index in drinking water distribution system using activation functions based Ann. Water Resour Manag 35(2):535–553

    Article  Google Scholar 

  • Wong YJ, Arumugasamy SK, Chung CH, Selvarajoo A, Sethu V (2020) Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel. Environ Monit Assess 192(7):439

    Article  CAS  Google Scholar 

  • Yari AR, Sobhanardakani S (2016) Water quality assessment of groundwater resources in Qaleeh Shahin Plain based on Cd and HEI. Int Arch Health Sci 3(3):101–106

    Article  CAS  Google Scholar 

  • Yuan Q, Xu H, Li T, Shen H, Zhang L (2020) Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental US. J Hydrol 580:124351

    Article  Google Scholar 

  • Zhang H, Wang J, Sun Z, Zurada JM, Pal NR (2019) Feature selection for neural networks using group lasso regularization. IEEE Trans Knowl Data Eng 32(4):659–673

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the Hamedan Branch, Islamic Azad University for providing facilities to conduct and complete this study.

Author information

Authors and Affiliations

  1. Department of the Environment, College of Basic Sciences, Hamedan Branch, Islamic Azad University, Hamedan, Iran

    Azadeh Ghobadi, Mehrdad Cheraghi, Soheil Sobhanardakani & Bahareh Lorestani

  2. Department of Agriculture, Sayyed Jamaleddin Asadabadi University, Asadabad, Iran

    Hajar Merrikhpour

Authors
  1. Azadeh Ghobadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehrdad Cheraghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Soheil Sobhanardakani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bahareh Lorestani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hajar Merrikhpour
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Azadeh Ghobadi, Mehrdad Cheraghi, Soheil Sobhan Ardakani, Bahareh Lorestani, and Hajar Merrikhpour. The first draft of the manuscript was written by Azadeh Ghobadi and Soheil Sobhan Ardakani, and all authors commented on previous versions of the manuscript. The corresponding author ensures that all listed authors have approved the manuscript before submission, including the names and order of authors.

Corresponding author

Correspondence to Mehrdad Cheraghi.

Ethics declarations

Ethics approval and consent to participate

This article does not contain any studies with animals and human subjects. The authors confirm that all the research meets ethical guidelines and adheres to the legal requirements of the study country.

Consent for publication

The authors declare that this manuscript does not contain any individual person’s data and material in any form.

Conflict of interest

The authors declare no competing interests.

Additional information

Responsible editor: Xianliang Yi

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghobadi, A., Cheraghi, M., Sobhanardakani, S. et al. Groundwater quality modeling using a novel hybrid data-intelligence model based on gray wolf optimization algorithm and multi-layer perceptron artificial neural network: a case study in Asadabad Plain, Hamedan, Iran. Environ Sci Pollut Res 29, 8716–8730 (2022). https://doi.org/10.1007/s11356-021-16300-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-021-16300-4

Keywords

Search

Navigation