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

Muhammad Izhar Shah, Taher Abunama, Muhammad Faisal Javed, Faizal Bux, Ali Aldrees, Muhammad Atiq Ur Rehman Tariq and Amir Mosavi
Additional contact information
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
Amir Mosavi: Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany

Sustainability, 2021, vol. 13, issue 8, 1-17

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.

Keywords: data-driven; outlier detection; machine learning; surface water quality; input optimization; neuro-fuzzy; water quality management; hydrology; artificial intelligence; big data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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