The Integration of Nature-Inspired Algorithms with Least Square Support Vector Regression Models: Application to Modeling River Dissolved Oxygen Concentration

Water. 2018;10(9):1124 DOI 10.3390/w10091124


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Journal Title: Water

ISSN: 2073-4441 (Print)

Publisher: MDPI AG

LCC Subject Category: Technology: Hydraulic engineering | Technology: Environmental technology. Sanitary engineering: Water supply for domestic and industrial purposes

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML



Zaher Mundher Yaseen (Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam)
Mohammad Ehteram (Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran)
Ahmad Sharafati (Civil Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Shamsuddin Shahid (School of Civil Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia)
Nadhir Al-Ansari (Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, Sweden)
Ahmed El-Shafie (Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)


Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks


Abstract | Full Text

The current study investigates an improved version of Least Square Support Vector Machines integrated with a Bat Algorithm (LSSVM-BA) for modeling the dissolved oxygen (DO) concentration in rivers. The LSSVM-BA model results are compared with those obtained using M5 Tree and Multivariate Adaptive Regression Spline (MARS) models to show the efficacy of this novel integrated model. The river water quality data at three monitoring stations located in the USA are considered for the simulation of DO concentration. Eight input combinations of four water quality parameters, namely, water temperature, discharge, pH, and specific conductance, are used to simulate the DO concentration. The results revealed the superiority of the LSSVM-BA model over the M5 Tree and MARS models in the prediction of river DO. The accuracy of the LSSVM-BA model compared with those of the M5 Tree and MARS models is found to increase by 20% and 42%, respectively, in terms of the root-mean-square error. All the predictive models are found to perform best when all the four water quality variables are used as input, which indicates that it is possible to supply more information to the predictive model by way of incorporation of all the water quality variables.