Prediction of Water Quality Index by Support Vector Machine: a Case Study in the Sefidrud Basin, Northern Iran

2019 ◽  
Vol 46 (1) ◽  
pp. 112-116 ◽  
Author(s):  
Forough Kamyab-Talesh ◽  
Seyed-Farhad Mousavi ◽  
Mohammadreza Khaledian ◽  
Ozra Yousefi-Falakdehi ◽  
Mojtaba Norouzi-Masir
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Water Quality Index ◽  
Quality Index ◽  
Support Vector ◽  
Northern Iran

scholarly journals Integration of advanced optimization algorithms into least-square support vector machine (LSSVM) for water quality index prediction

2021 ◽  
Author(s):  
See Leng Chia ◽  
Min Yan Chia ◽  
Chai Hoon Koo ◽  
Yuk Feng Huang
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Water Quality Index ◽  
Quality Index ◽  
Optimization Algorithms ◽  
Least Square ◽  
Support Vector ◽  
Hybrid Particle Swarm Optimization ◽  
Mutation Strategy ◽  
Whale Optimization

Abstract Machine learning models hybridized with optimization algorithms have been applied to many real-life applications, including the prediction of water quality. However, the emergence of newly developed advanced algorithms can provide new scopes and possibilities for further enhancements. In this study, the least-square support vector machine (LSSVM) integrated with advanced optimization algorithms is presented, for the first time, in the prediction of water quality index (WQI) at the Klang River of Malaysia. Thereafter, the LSSVM model using RBF kernel was optimized using the hybrid particle swarm optimization and genetic algorithm (HPSOGA), whale optimization based on self-adapting parameter adjustment and mix mutation strategy (SMWOA) as well as ameliorative moth-flame optimization (AMFO) separately. It was found that the SMWOA-LSSVM model had the better performance for WQI prediction by having the best achievement root means square error (RMSE), mean absolute error (MAE), coefficient of determination (R2) and mean absolute percentage error (MAPE). Comprehensive comparison was done using the global performance indicator (GPI), whereby the SMWOA-LSSVM had the highest average score of 0.31. This could be attributed to the internal architecture of the SMWOA, which was catered to avoid local optima within short optimization period.

Prediction of water quality index in constructed wetlands using support vector machine

2014 ◽  
Vol 22 (8) ◽  
pp. 6208-6219 ◽  
Author(s):  
Reza Mohammadpour ◽  
Syafiq Shaharuddin ◽  
Chun Kiat Chang ◽  
Nor Azazi Zakaria ◽  
Aminuddin Ab Ghani ◽  
...  
Keyword(s):  
Water Quality ◽  
Support Vector Machine ◽  
Constructed Wetlands ◽  
Water Quality Index ◽  
Quality Index ◽  
Support Vector
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