A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters

Abstract

In this study, a novel least square support vector machine (LSSVM) model integrated with gradient-based optimizer (GBO) algorithm is introduced for assessment of water quality parameters. For this purpose, three stations including Ahvaz, Armand, and Gotvand in the Karun river basin have been selected to model electrical conductivity (EC), and total dissolved solids (TDS). First, to prove the superiority of the LSSVM-GBO algorithm, the performance is evaluated with three benchmark datasets (Housing, LVST, Servo). Then, the results of the new hybrid algorithm were compared with those of artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and LSSVM algorithms. Input combination for assessment of water quality parameters EC and TDS consists of Ca + 2 , Cl -1 , Mg + 2 , Na + 1 , SO4, HCO3, sodium absorption ratio (SAR), sum cation (Sum.C), sum anion (Sum.A), PH, and Q. The modelling results based on evaluation criteria showed the significant performance of LSSVM-GBO among all benchmark datasets and algorithms. Other results showed that in Ahvaz station, Sum.C, Sum.A, and Na +1 parameters, and in Gotvand and Armand stations, Sum.C, Sum.A, and Cl -1 parameters have the greatest impact on modelling EC and TDS parameters. In the next step, EC and TDS modelling was performed based on the best input combination and the best algorithm in Mojtaba Kadkhodazadeh

A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

“…In the present study, various evaluation criteria, including mean absolute error (MAE), root mean square error (RMSE), person correlation coefficient (R), mean absolute relative error (MARE) and relative root mean square error (RRMSE) are employed for evaluation of machine learning algorithms. The mentioned evaluation criteria are given in Appendix A.7 [60][61][62].…”

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

“…Kadkhodazadeh and Farzin [86] explored a novel gradient-based optimiser (GBO) algorithm coupled with a least square support vector machine (LSSVM) technique for the evaluation of WQ parameters. The LSSVM-GBO method's performance is examined using three benchmark datasets to demonstrate its superiority (Housing, LVST, Servo).…”

“…The following supporting information can be downloaded at: https://www. mdpi.com/article/10.3390/environments9070085/s1, Table S1: Abbreviations; Table S2: Review of researchers who used data pre-processing [4,16,23,28,38,[56][57][58][59][60][67][68][69][73][74][75]78,80,81,83,[85][86][87].…”

An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment

“…In (Kim et al 2021 ), artificial neural network was applied to construct the WQI for water quality assessment. The results show that stepwise multiple linear regression analysis and artificial neural network can build an accurate WQI by using few water quality parameters (Gebler et al 2018 ; Kadkhodazadeh and Farzin 2021 ). However, these methods will assign the negative weight or the extreme weight to the water quality parameter, which will lose the physical meaning, lead to overfitting problem and show low generalization ability.…”