IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i12d10.1007_s11269-021-02913-4.html
   My bibliography  Save this article

A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters

Author

Listed:
  • Mojtaba Kadkhodazadeh

    (Semnan University)

  • Saeed Farzin

    (Semnan University)

Abstract

In this study, a novel least square support vector machine (LSSVM) model integrated with gradient-based optimizer (GBO) algorithm is introduced for the assessment of water quality (WQ) parameters. For this purpose, three stations, including Ahvaz, Armand, and Gotvand in the Karun river basin, have been selected to model electrical conductivity (EC) and total dissolved solids (TDS). First, to prove the superiority of the LSSVM-GBO algorithm, the performance is evaluated with three benchmark datasets (Housing, LVST, Servo). Then, the results of the new hybrid algorithm were compared with those of artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and LSSVM algorithms. Input combination for assessment of WQ parameters EC and TDS consists of Ca+2, Cl−1, Mg+2, Na+1, SO4, HCO3, sodium absorption ratio (SAR), sum cation (Sum.C), sum anion (Sum.A), pH, and Q. The modeling results based on evaluation criteria showed the significant performance of LSSVM-GBO among all benchmark datasets and algorithms. Other results showed that in Ahvaz station, Sum.C, Sum.A, and Na+1 parameters, and in Armand and Gotvand stations, Sum.C, Sum.A, and Cl−1 parameters have the greatest impact on modeling EC and TDS parameters. Then, EC and TDS modeling was performed based on the best input combination and the best algorithm in different time delays. The highest accuracy of modeling EC and TDS parameters in Gotvand station was and C1 time delay.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:12:d:10.1007_s11269-021-02913-4
    DOI: 10.1007/s11269-021-02913-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-021-02913-4
    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/s11269-021-02913-4?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. Kwan Lee & Wei-Chiao Hung & Chung-Chieh Meng, 2008. "Deterministic Insight into ANN Model Performance for Storm Runoff Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(1), pages 67-82, January.
    2. 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.
    3. Mahdi Valikhan Anaraki & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2021. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 199-223, January.
    4. Ashish Kumar & Pravendra Kumar & Vijay Kumar Singh, 2019. "Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1217-1231, February.
    5. S. Vijay & K. Kamaraj, 2021. "Prediction of Water Quality Index in Drinking Water Distribution System Using Activation Functions Based Ann," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(2), pages 535-553, January.
    6. Zaher Mundher Yaseen & Mazen Ismaeel Ghareb & Isa Ebtehaj & Hossein Bonakdari & Ridwan Siddique & Salim Heddam & Ali A. Yusif & Ravinesh Deo, 2018. "Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 105-122, January.
    7. Sinan Jasim Hadi & Mustafa Tombul, 2018. "Streamflow Forecasting Using Four Wavelet Transformation Combinations Approaches with Data-Driven Models: A Comparative Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4661-4679, November.
    8. Mohammad Ali Baghapour & Mohammad Reza Shooshtarian & Mahdi Zarghami, 2020. "Process Mining Approach of a New Water Quality Index for Long-Term Assessment under Uncertainty Using Consensus-Based Fuzzy Decision Support System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1155-1172, February.
    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. Mojtaba Kadkhodazadeh & Saeed Farzin, 2022. "Introducing a Novel Hybrid Machine Learning Model and Developing its Performance in Estimating Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3901-3927, August.
    2. Mustafa Al-Mukhtar & Aman Srivastava & Leena Khadke & Tariq Al-Musawi & Ahmed Elbeltagi, 2024. "Prediction of Irrigation Water Quality Indices Using Random Committee, Discretization Regression, REPTree, and Additive Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 343-368, January.
    3. Stefan Tsokov & Milena Lazarova & Adelina Aleksieva-Petrova, 2022. "A Hybrid Spatiotemporal Deep Model Based on CNN and LSTM for Air Pollution Prediction," Sustainability, MDPI, vol. 14(9), pages 1-38, April.
    4. Jingjing Xia & Jin Zeng, 2022. "Environmental Factors Assisted the Evaluation of Entropy Water Quality Indices with Efficient Machine Learning Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2045-2060, April.
    5. Sufyan Ghani & Sunita Kumari, 2022. "Liquefaction behavior of Indo-Gangetic region using novel metaheuristic optimization algorithms coupled with artificial neural network," 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. 111(3), pages 2995-3029, April.
    6. Mojtaba Kadkhodazadeh & Mahdi Valikhan Anaraki & Amirreza Morshed-Bozorgdel & Saeed Farzin, 2022. "A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods," Sustainability, MDPI, vol. 14(5), pages 1-37, February.
    7. Mojtaba Poursaeid & Amir Houssain Poursaeid & Saeid Shabanlou, 2022. "A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(5), pages 1499-1519, March.
    8. Zehai Gao & Yang Liu & Nan Li & Kangjie Ma, 2022. "An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2685-2702, June.

    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. Mahdi Valikhan Anaraki & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2021. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 199-223, January.
    2. Xuan Wang & Wenchong Tian & Zhenliang Liao, 2022. "Framework for Hyperparameter Impact Analysis and Selection for Water Resources Feedforward Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4201-4217, September.
    3. Rana Muhammad Adnan & Kulwinder Singh Parmar & Salim Heddam & Shamsuddin Shahid & Ozgur Kisi, 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    4. Yongxiang Zhang & Ruitao Jia & Jin Wu & Huaqing Wang & Zhuoran Luo, 2021. "Evaluation of Groundwater Using an Integrated Approach of Entropy Weight and Stochastic Simulation: A Case Study in East Region of Beijing," IJERPH, MDPI, vol. 18(14), pages 1-18, July.
    5. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    6. Sahebkar Farkhani, Jalal & Çelik, Özgür & Ma, Kaiqi & Bak, Claus Leth & Chen, Zhe, 2024. "A comprehensive review of potential protection methods for VSC multi-terminal HVDC systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    7. Manish Goyal & C. Ojha, 2011. "Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2177-2195, July.
    8. Parvin Golfam & Parisa-Sadat Ashofteh, 2022. "Performance Indexes Analysis of the Reservoir-Hydropower Plant System Affected by Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5127-5162, October.
    9. Zehai Gao & Yang Liu & Nan Li & Kangjie Ma, 2022. "An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2685-2702, June.
    10. Chao Liu & Mingshuang Xu & Yufeng Liu & Xuefei Li & Zonglin Pang & Sheng Miao, 2022. "Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study," IJERPH, MDPI, vol. 19(23), pages 1-14, November.
    11. Shan-e-hyder Soomro & Muhammad Waseem Boota & Xiaotao Shi & Gul-e-Zehra Soomro & Yinghai Li & Muhammad Tayyab & Caihong Hu & Chengshuai Liu & Yuanyang Wang & Junaid Abdul Wahid & Mairaj Hyder Alias Aa, 2024. "Appraisal of Urban Waterlogging and Extent Damage Situation after the Devastating Flood," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4911-4931, September.
    12. Desalegn Edossa & Mukand Babel, 2011. "Application of ANN-Based Streamflow Forecasting Model for Agricultural Water Management in the Awash River Basin, Ethiopia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1759-1773, April.
    13. Lingqi Li & Kai Wu & Enhui Jiang & Huijuan Yin & Yuanjian Wang & Shimin Tian & Suzhen Dang, 2021. "Evaluating Runoff-Sediment Relationship Variations Using Generalized Additive Models That Incorporate Reservoir Indices for Check Dams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3845-3860, September.
    14. Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.
    15. Mingxiang Yang & Hao Wang & Yunzhong Jiang & Xing Lu & Zhao Xu & Guangdong Sun, 2020. "GECA Proposed Ensemble–KNN Method for Improved Monthly Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 849-863, January.
    16. 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.
    17. 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.
    18. Monika Kulisz & Justyna Kujawska & Bartosz Przysucha & Wojciech Cel, 2021. "Forecasting Water Quality Index in Groundwater Using Artificial Neural Network," Energies, MDPI, vol. 14(18), pages 1-17, September.
    19. Vahid Gholami & Mohammad Reza Khaleghi, 2021. "A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 67(4), pages 165-174.
    20. Bhuvanamitra Sulugodu & Paresh Chandra Deka, 2019. "Evaluating the Performance of CHIRPS Satellite Rainfall Data for Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3913-3927, September.

    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:35:y:2021:i:12:d:10.1007_s11269-021-02913-4. 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.