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Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization

Author

Listed:
  • Muhammad Izhar Shah

    (Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan)

  • Taher Abunama

    (Institute for Water and Wastewater Technology, Durban University of Technology, Durban 4001, South Africa)

  • Muhammad Faisal Javed

    (Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan)

  • Faizal Bux

    (Institute for Water and Wastewater Technology, Durban University of Technology, Durban 4001, South Africa)

  • Ali Aldrees

    (Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia)

  • Muhammad Atiq Ur Rehman Tariq

    (Institute for Sustainable Industries & Liveable Cities, Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia
    College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia)

  • Amir Mosavi

    (Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
    Department of Ecology, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany
    John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary)

Abstract

Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the developed models was estimated by statistical assessment indicators. Moreover, the 10-fold cross-validation method was carried out to address the modeling overfitting issue. The results of the input optimization indicate that Ca 2+ , Na + , and Cl − are the most relevant inputs to be used for EC. Meanwhile, Mg 2+ , HCO 3 − , and SO 4 2− were selected to model TDS levels. The optimum ANFIS models for the EC and TDS data showed R values of 0.91 and 0.92, and the root mean squared error (RMSE) results of 30.6 µS/cm and 16.7 ppm, respectively. The optimum ANFIS structure comprises a hybrid training algorithm with 27 fuzzy rules of triangular fuzzy membership functions for EC and a Gaussian curve for TDS modeling, respectively. Evidently, the outcome of the present study reveals that the ANFIS modeling, aided with data pre-processing and input optimization, is a suitable technique for simulating the quality of surface water. It could be an effective approach in minimizing modeling complexity and elaborating proper management and mitigation measures.

Suggested Citation

  • Muhammad Izhar Shah & Taher Abunama & Muhammad Faisal Javed & Faizal Bux & Ali Aldrees & Muhammad Atiq Ur Rehman Tariq & Amir Mosavi, 2021. "Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4576-:d:539688
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    References listed on IDEAS

    as
    1. Muhammad Izhar Shah & Muhammad Nasir Amin & Kaffayatullah Khan & Muhammad Sohaib Khan Niazi & Fahid Aslam & Rayed Alyousef & Muhammad Faisal Javed & Amir Mosavi, 2021. "Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete," Sustainability, MDPI, vol. 13(5), pages 1-20, March.
    2. Guangpei Sun & Peng Jiang & Huan Xu & Shanen Yu & Dong Guo & Guang Lin & Hui Wu, 2019. "Outlier Detection and Correction for Monitoring Data of Water Quality Based on Improved VMD and LSSVM," Complexity, Hindawi, vol. 2019, pages 1-12, February.
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    Cited by:

    1. Muhammad Izhar Shah & Wesam Salah Alaloul & Abdulaziz Alqahtani & Ali Aldrees & Muhammad Ali Musarat & Muhammad Faisal Javed, 2021. "Predictive Modeling Approach for Surface Water Quality: Development and Comparison of Machine Learning Models," Sustainability, MDPI, vol. 13(14), pages 1-20, July.

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