IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v39y2025i3d10.1007_s11269-024-04027-z.html
   My bibliography  Save this article

Optimization of Extreme Learning Machine with Metaheuristic Algorithms for Modelling Water Quality Parameters of Tamburawa Water Treatment Plant in Nigeria

Author

Listed:
  • Sani I. Abba

    (Prince Mohammad Bin Fahd University)

  • Quoc Bao Pham

    (University of Silesia in Katowice)

  • Anurag Malik

    (Punjab Agricultural University, Regional Research Station)

  • Romulus Costache

    (Department of Civil Engineering, Transilvania University of Brasov)

  • Muhammad Sani Gaya

    (Kano University of Science and Technology)

  • Jazuli Abdullahi

    (Baze University)

  • Sagiru Mati

    (Near East University)

  • A. G. Usman

    (Near East University)

  • Gaurav Saini

    (Netaji Subhas University Technology)

Abstract

Sustainable management of available water resources needs robust and reliable intelligent tools to address emerging water challenges. These days, artificial intelligence (AI) based tools are more efficient and prominent in addressing issues related to water treatment plants. Therefore, in the current study, the extreme learning machine (ELM) was optimized with four different metaheuristic algorithms, namely particle swarm optimization (PSO-ELM), genetic algorithm (GA-ELM), biogeography-based optimization (BBO-ELM), and BBO-PSO-ELM for modelling treated water quality parameters, i.e., pHT, Turbidity (TurbT), total dissolved solids (TDST), and HardnessT of Tamburawa water treatment plant (TWTP) located in Nigeria. The performance of the hybrid ELM models was evaluated using mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI) as well as graphically. The obtained numerical and visualized results indicate that the BBO-PSO-ELM model performed superior in modeling pHT (MAE = 0.403, RMSE = 0.514, NSE = 0.863, PCC = 0.935, WI = 0.964), TDST (MAE = 11.818 mg/L, RMSE = 16.058 mg/L, NSE = 0.711, PCC = 0.853, WI = 0.923), and HardnessT (MAE = 2.624 mg/L, RMSE = 3.497 mg/L, NSE = 0.818, PCC = 0.909, WI = 0.947), while BBO-ELM demonstrated superior performance in TurbT (MAE = 0.385 mg/L, RMSE = 0.694 mg/L, NSE = 0.996, PCC = 0.999, WI = 0.999) modelling. Generally, the findings suggested that the proposed hybrid ELM model has the potential to predict the water quality parameters of TWTP in Nigeria effectively.

Suggested Citation

  • Sani I. Abba & Quoc Bao Pham & Anurag Malik & Romulus Costache & Muhammad Sani Gaya & Jazuli Abdullahi & Sagiru Mati & A. G. Usman & Gaurav Saini, 2025. "Optimization of Extreme Learning Machine with Metaheuristic Algorithms for Modelling Water Quality Parameters of Tamburawa Water Treatment Plant in Nigeria," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(3), pages 1377-1401, February.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:3:d:10.1007_s11269-024-04027-z
    DOI: 10.1007/s11269-024-04027-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-04027-z
    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/s11269-024-04027-z?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. Lisheng Wei & Ning Wang & Huacai Lu & Shi Cheng, 2021. "A Novel BBO Algorithm Based on Local Search and Nonuniform Variation for Iris Classification," Complexity, Hindawi, vol. 2021, pages 1-17, April.
    2. Deepak Sinwar & Monika Saini & Dilbag Singh & Drishty Goyal & Ashish Kumar, 2021. "Availability and performance optimization of physical processing unit in sewage treatment plant using genetic algorithm and particle swarm optimization," 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. 12(6), pages 1235-1246, December.
    3. Quoc Bao Pham & S. I. Abba & Abdullahi Garba Usman & Nguyen Thi Thuy Linh & Vivek Gupta & Anurag Malik & Romulus Costache & Ngoc Duong Vo & Doan Quang Tri, 2019. "Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5067-5087, December.
    4. Mohammed Benaafi & Mohamed A. Yassin & A. G. Usman & S. I. Abba, 2022. "Neurocomputing Modelling of Hydrochemical and Physical Properties of Groundwater Coupled with Spatial Clustering, GIS, and Statistical Techniques," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    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. Bassam Tawabini & Mohamed A. Yassin & Mohammed Benaafi & John Adedapo Adetoro & Abdulaziz Al-Shaibani & S. I. Abba, 2022. "Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling," Sustainability, MDPI, vol. 14(4), pages 1-20, February.
    2. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    3. Jesmeen Mohd Zebaral Hoque & Nor Azlina Ab. Aziz & Salem Alelyani & Mohamed Mohana & Maruf Hosain, 2022. "Improving Water Quality Index Prediction Using Regression Learning Models," IJERPH, MDPI, vol. 19(20), pages 1-23, October.
    4. Yuliia Trach & Roman Trach & Marek Kalenik & Eugeniusz Koda & Anna Podlasek, 2021. "A Study of Dispersed, Thermally Activated Limestone from Ukraine for the Safe Liming of Water Using ANN Models," Energies, MDPI, vol. 14(24), pages 1-14, December.
    5. Justyna Kujawska & Monika Kulisz & Piotr Oleszczuk & Wojciech Cel, 2023. "Improved Prediction of the Higher Heating Value of Biomass Using an Artificial Neural Network Model Based on the Selection of Input Parameters," Energies, MDPI, vol. 16(10), pages 1-16, May.
    6. Muhammad Ishfaque & Qianwei Dai & Nuhman ul Haq & Khanzaib Jadoon & Syed Muzyan Shahzad & Hammad Tariq Janjuhah, 2022. "Use of Recurrent Neural Network with Long Short-Term Memory for Seepage Prediction at Tarbela Dam, KP, Pakistan," Energies, MDPI, vol. 15(9), pages 1-16, April.
    7. Jamshid Piri & Mohammad Abdolahipour & Behrooz Keshtegar, 2023. "Advanced Machine Learning Model for Prediction of Drought Indices using Hybrid SVR-RSM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 683-712, January.
    8. Karbasi, Masoud & Jamei, Mehdi & Malik, Anurag & Kisi, Ozgur & Yaseen, Zaher Mundher, 2023. "Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model," Agricultural Water Management, Elsevier, vol. 281(C).
    9. Mousumi Banerjee & Vanita Garg & Kusum Deep, 2023. "Solving structural and reliability optimization problems using efficient mutation strategies embedded in sine cosine algorithm," 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. 14(1), pages 307-327, March.
    10. Gebre Gelete, 2023. "Hybrid Extreme Gradient Boosting and Nonlinear Ensemble Models for Suspended Sediment Load Prediction in an Agricultural Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(14), pages 5759-5787, November.
    11. Rocio Camarena-Martinez & Rocio A. Lizarraga-Morales & Roberto Baeza-Serrato, 2021. "Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network," Energies, MDPI, vol. 14(21), pages 1-13, November.
    12. George Halkos, 2023. "Economic Analysis and Policies for the Environment, Natural Resources, and Energy," Energies, MDPI, vol. 16(18), pages 1-6, September.
    13. Rana Muhammad Adnan & Sarita Gajbhiye Meshram & Reham R. Mostafa & Abu Reza Md. Towfiqul Islam & S. I. Abba & Francis Andorful & Zhihuan Chen, 2023. "Application of Advanced Optimized Soft Computing Models for Atmospheric Variable Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-29, March.
    14. Maryam Rahimzad & Alireza Moghaddam Nia & Hosam Zolfonoon & Jaber Soltani & Ali Danandeh Mehr & Hyun-Han Kwon, 2021. "Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4167-4187, September.
    15. Roman Trach & Yuliia Trach & Agnieszka Kiersnowska & Anna Markiewicz & Marzena Lendo-Siwicka & Konstantin Rusakov, 2022. "A Study of Assessment and Prediction of Water Quality Index Using Fuzzy Logic and ANN Models," Sustainability, MDPI, vol. 14(9), pages 1-19, May.
    16. Tao, Hai & Alawi, Omer A. & Kamar, Haslinda Mohamed & Nafea, Ahmed Adil & AL-Ani, Mohammed M. & Abba, Sani I. & Salami, Babatunde Abiodun & Oudah, Atheer Y. & Mohammed, Mustafa K.A., 2024. "Development of integrative data intelligence models for thermo-economic performances prediction of hybrid organic rankine plants," Energy, Elsevier, vol. 292(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:39:y:2025:i:3:d:10.1007_s11269-024-04027-z. 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.