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Article

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

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Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan
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Institute for Water and Wastewater Technology, Durban University of Technology, Durban 4001, South Africa
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Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
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Institute for Sustainable Industries & Liveable Cities, Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia
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College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia
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Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
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Department of Ecology, Technische Universität Kaiserslautern, 67663 Kaiserslautern, Germany
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John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
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Authors to whom correspondence should be addressed.
Academic Editors: Daeryong Park, Momcilo Markus and Myoung-Jin Um
Sustainability 2021, 13(8), 4576; https://0-doi-org.brum.beds.ac.uk/10.3390/su13084576
Received: 4 March 2021 / Revised: 13 April 2021 / Accepted: 13 April 2021 / Published: 20 April 2021
(This article belongs to the Special Issue Modeling and Simulations for Sustainable Water Environments)
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 Ca2+, Na+, and Cl are the most relevant inputs to be used for EC. Meanwhile, Mg2+, HCO3, and SO42− 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. View Full-Text
Keywords: data-driven; outlier detection; machine learning; surface water quality; input optimization; neuro-fuzzy; water quality management; hydrology; artificial intelligence; big data data-driven; outlier detection; machine learning; surface water quality; input optimization; neuro-fuzzy; water quality management; hydrology; artificial intelligence; big data
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MDPI and ACS Style

Shah, M.I.; Abunama, T.; Javed, M.F.; Bux, F.; Aldrees, A.; Tariq, M.A.U.R.; Mosavi, A. Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization. Sustainability 2021, 13, 4576. https://0-doi-org.brum.beds.ac.uk/10.3390/su13084576

AMA Style

Shah MI, Abunama T, Javed MF, Bux F, Aldrees A, Tariq MAUR, Mosavi A. Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization. Sustainability. 2021; 13(8):4576. https://0-doi-org.brum.beds.ac.uk/10.3390/su13084576

Chicago/Turabian Style

Shah, Muhammad I., Taher Abunama, Muhammad F. Javed, Faizal Bux, Ali Aldrees, Muhammad A.U.R. Tariq, and Amir Mosavi. 2021. "Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization" Sustainability 13, no. 8: 4576. https://0-doi-org.brum.beds.ac.uk/10.3390/su13084576

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