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

Mohammad Hijji, Tzu-Chia Chen, Muhammad Ayaz, Ali S. Abosinnee, Iskandar Muda, Yury Razoumny and Javad Hatamiafkoueieh ()
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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
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

Sustainability, 2023, vol. 15, issue 8, 1-23

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 ).

Keywords: total dissolved solids; physiochemical parameters; Fuzzy-AI models; Grasshopper optimization algorithm; coastal region (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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