IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v33y2019i2d10.1007_s11269-018-2147-6.html
   My bibliography  Save this article

Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods

Author

Listed:
  • Ozgur Kisi

    (Ilia State University)

  • Armin Azad

    (Semnan University)

  • Hamed Kashi

    (Technology University of Munich)

  • Amir Saeedian

    (Semnan University)

  • Seyed Ali Asghar Hashemi

    (Agriculture Research and Education Organization - Natural Resources Research Center of Semnan Province)

  • Salar Ghorbani

    (Semnan University)

Abstract

In this study, the application of four evolutionary algorithms, continuous genetic algorithm (CGA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR), and differential evolution (DE) were considered for training and optimization of adaptive neuro-fuzzy inference system (ANFIS) to model groundwater quality variables. At first, using correlation and sensitivity analysis, the best inputs were selected to estimate electrical conductivity (EC), sodium adsorption ratio (SAR) and total hardness (TH). After that, the quality variables were modeled by simple ANFIS and the ANFIS trained by evolutionary algorithms. Finally, the models’ performances were evaluated using determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) and sensitivity analysis. Results indicated that: 1) All the suggested algorithms improved the ANFIS performance in the modeling of EC and TH. Also, in SAR, CGA and PSO had a better performance than existing algorithms of ANFIS. 2) CGA with the most appropriate results, was the best algorithm in improving ANFIS performance for modeling the groundwater quality variables such that the amounts of R2, RMSE, and MAPE were improved by 0.14, 35.4, and 0.59 for TH, by 0.13, 226 (μmho Cm−1), 2.16 for EC, and by 0.15, 690, and 19.04 for SAR, respectively. 3) Sensitivity analysis showed that the results obtained by correlation analysis was dependable and could be used as a primary step in choosing the best input data for prediction of groundwater quality variables.

Suggested Citation

  • Ozgur Kisi & Armin Azad & Hamed Kashi & Amir Saeedian & Seyed Ali Asghar Hashemi & Salar Ghorbani, 2019. "Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 847-861, January.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:2:d:10.1007_s11269-018-2147-6
    DOI: 10.1007/s11269-018-2147-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-018-2147-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-018-2147-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Masoomeh Mirrashid, 2014. "Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1577-1593, December.
    2. Gebdang B. Ruben & Ke Zhang & Hongjun Bao & Xirong Ma, 2018. "Application and Sensitivity Analysis of Artificial Neural Network for Prediction of Chemical Oxygen Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 273-283, January.
    3. Atul Anand & L. Suganthi, 2017. "Forecasting of Electricity Demand by Hybrid ANN-PSO Models," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 6(4), pages 66-83, October.
    4. Socha, Krzysztof & Dorigo, Marco, 2008. "Ant colony optimization for continuous domains," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1155-1173, March.
    5. Seyed Akrami & Ahmed El-Shafie & Othman Jaafar, 2013. "Improving Rainfall Forecasting Efficiency Using Modified Adaptive Neuro-Fuzzy Inference System (MANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3507-3523, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.
    2. Ankita P. Dadhich & Rohit Goyal & Pran N. Dadhich, 2021. "Assessment and Prediction of Groundwater using Geospatial and ANN Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2879-2893, July.
    3. Mojtaba Kadkhodazadeh & Saeed Farzin, 2021. "A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3939-3968, September.
    4. Naser Shiri & Jalal Shiri & Zaher Mundher Yaseen & Sungwon Kim & Il-Moon Chung & Vahid Nourani & Mohammad Zounemat-Kermani, 2021. "Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-24, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sutapa Chaudhuri & Arumita Roy Chowdhury & Payel Das, 2018. "Implementation of Sugeno: ANFIS for forecasting the seismic moment of large earthquakes over Indo-Himalayan region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(1), pages 391-405, January.
    2. Bera, Sasadhar & Mukherjee, Indrajit, 2016. "A multistage and multiple response optimization approach for serial manufacturing system," European Journal of Operational Research, Elsevier, vol. 248(2), pages 444-452.
    3. Mohammed Seyam & Faridah Othman & Ahmed El-Shafie, 2017. "RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 187-204, January.
    4. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    5. Sami Abdullah Osman & Meshal Almoshaogeh & Arshad Jamal & Fawaz Alharbi & Abdulhamid Al Mojil & Muhammad Abubakar Dalhat, 2022. "Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks," Sustainability, MDPI, vol. 15(1), pages 1-30, December.
    6. Anand Kumar & Manoj Thakur & Garima Mittal, 2018. "A new ants interaction scheme for continuous optimization problems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 784-801, August.
    7. Nikolaos Ploskas & Nikolaos V. Sahinidis, 2022. "Review and comparison of algorithms and software for mixed-integer derivative-free optimization," Journal of Global Optimization, Springer, vol. 82(3), pages 433-462, March.
    8. Bera, Sasadhar & Mukherjee, Indrajit, 2012. "An ellipsoidal distance-based search strategy of ants for nonlinear single and multiple response optimization problems," European Journal of Operational Research, Elsevier, vol. 223(2), pages 321-332.
    9. Amjad Hudaib & Mohammad Khanafseh & Ola Surakhi, 2018. "An Improved Version of K-medoid Algorithm using CRO," Modern Applied Science, Canadian Center of Science and Education, vol. 12(2), pages 116-116, February.
    10. Liao, Tianjun & Stützle, Thomas & Montes de Oca, Marco A. & Dorigo, Marco, 2014. "A unified ant colony optimization algorithm for continuous optimization," European Journal of Operational Research, Elsevier, vol. 234(3), pages 597-609.
    11. Kulwinder Parmar & Rashmi Bhardwaj, 2015. "River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(1), pages 17-33, January.
    12. Eroğlu, Yunus & Seçkiner, Serap Ulusam, 2012. "Design of wind farm layout using ant colony algorithm," Renewable Energy, Elsevier, vol. 44(C), pages 53-62.
    13. Martin Schlüter & Matthias Gerdts, 2010. "The oracle penalty method," Journal of Global Optimization, Springer, vol. 47(2), pages 293-325, June.
    14. Luo, Qifang & Yang, Xiao & Zhou, Yongquan, 2019. "Nature-inspired approach: An enhanced moth swarm algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 57-92.
    15. Tiago Maritan Ugulino Araújo & Lisieux Marie M. S. Andrade & Carlos Magno & Lucídio Anjos Formiga Cabral & Roberto Quirino Nascimento & Cláudio N. Meneses, 2016. "DC-GRASP: directing the search on continuous-GRASP," Journal of Heuristics, Springer, vol. 22(4), pages 365-382, August.
    16. Shao, Peng & Liang, Ying & Li, Guangquan & Li, Xing & Yang, Le, 2023. "Birefringence learning: A new global optimization technology model based on birefringence principle in application on artificial bee colony," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 470-486.
    17. Andres Quiros-Granados & JAvier Trejos-Zelaya, 2019. "Estimation of the yield curve for Costa Rica using combinatorial optimization metaheuristics applied to nonlinear regression," Papers 2001.00920, arXiv.org.
    18. Gokmen Tayfur & Ata Nadiri & Asghar Moghaddam, 2014. "Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1173-1184, March.
    19. Suiling Wang & Zhiqiang Jiang & Hairong Zhang, 2022. "Correction of Reservoir Runoff Forecast Based on Multi-scenario Division and Multi Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5277-5296, October.
    20. Jelić, Marko & Batić, Marko & Krstić, Aleksandra & Bottarelli, Michele & Mainardi, Elena, 2023. "Comparative analysis of metaheuristic optimization approaches for multisource heat pump operation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:33:y:2019:i:2:d:10.1007_s11269-018-2147-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.