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Optimization of State of the Art Fuzzy-Based Machine Learning Techniques for Total Dissolved Solids Prediction

Author

Listed:
  • Mohammad Hijji

    (Faculty of Computers and Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia)

  • Tzu-Chia Chen

    (College of Management and Design, Ming Chi University of Technology, New Taipei City 243303, Taiwan)

  • Muhammad Ayaz

    (Sensor Networks and Cellular Systems (SNCS) Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia)

  • Ali S. Abosinnee

    (Quality Assurance Department, Altoosi University College, Najaf, Iraq
    Quality Assurance Department, The Islamic University, Najaf, Iraq)

  • Iskandar Muda

    (Department of Doctoral Program, Faculty Economic and Business, Universitas Sumatera Utara, Medan 20222, Indonesia)

  • Yury Razoumny

    (Department of Mechanics and Control Processes, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), Miklukho-Maklaya Str. 6, Moscow 117198, Russia)

  • Javad Hatamiafkoueieh

    (Department of Mechanics and Control Processes, Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), Miklukho-Maklaya Str. 6, Moscow 117198, Russia)

Abstract

Total dissolved solid prediction is an important factor which can support the early warning of water pollution, especially in the areas exposed to a mixture of pollutants. In this study, a new fuzzy-based intelligent system was developed, due to the uncertainty of the TDS time series data, by integrating optimization algorithms. Monthly-timescale water quality parameters data from nearly four decades (1974–2016), recorded over two gaging stations in coastal Iran, were used for the analysis. For model implementation, the current research aims to model the TDS parameter in a river system by using relevant biochemical parameters such as Ca, Mg, Na, and HCO 3 . To produce more compact networks along with the model’s generalization, a hybrid model which integrates a fuzzy-based intelligent system with the grasshopper optimization algorithm, NF-GMDH-GOA, is proposed for the prediction of the monthly TDS, and the prediction results are compared with five standalone and hybrid machine learning techniques. Results show that the proposed integrated NF-GMDH-GOA was able to provide an algorithmically informed simulation (NSE = 0.970 for Rig-Cheshmeh and NSE = 0.94 Soleyman Tangeh) of the dynamics of TDS records comparable to the artificial neural network, extreme learning machine, adaptive neuro fuzzy inference system, GMDH, and NF-GMDH-PSO models. According to the results of sensitivity analysis, Sodium in natural bodies of water with maximum value of error (RMSE = 56.4) had the highest influence on the TDS prediction for both stations, and Mg with RMSE = 43.251 stood second. The results of the Wilcoxon signed rank tests also indicated that the model’s prediction means were different, as the p value calculated for the models was less than the standard significance level ( α = 0.05 ).

Suggested Citation

  • Mohammad Hijji & Tzu-Chia Chen & Muhammad Ayaz & Ali S. Abosinnee & Iskandar Muda & Yury Razoumny & Javad Hatamiafkoueieh, 2023. "Optimization of State of the Art Fuzzy-Based Machine Learning Techniques for Total Dissolved Solids Prediction," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:7016-:d:1129653
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    References listed on IDEAS

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    1. Majid Dehghani & Hossein Riahi-Madvar & Farhad Hooshyaripor & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Kwok-wing Chau, 2019. "Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 12(2), pages 1-20, January.
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    3. Vahid Moosavi & Ali Talebi & Mohammad Reza Hadian, 2017. "Development of a Hybrid Wavelet Packet- Group Method of Data Handling (WPGMDH) Model for Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 43-59, January.
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