A Novel Multiple-Kernel Support Vector Regression Algorithm for Estimation of Water Quality Parameters

The quality of surface waters plays a key role in the sustainability of ecological systems. Measuring water quality parameters (WQPs) is of high importance in the management of surface water resources. In this paper, contemporary-developed regression analysis was proposed to estimate the hard-to-mea...

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Bibliographic Details
Published in:	Natural resources research (New York, N.Y.) Vol. 30; no. 5; pp. 3761 - 3775
Main Authors:	Najafzadeh, Mohammad, Niazmardi, Saeid
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.10.2021 Springer Nature B.V
Subjects:	Algorithms Biochemical oxygen demand Calcium phosphates Chemical oxygen demand Chemistry and Earth Sciences Computer Science Correlation coefficient Correlation coefficients Earth and Environmental Science Earth Sciences Electrical conductivity Electrical resistivity Fossil Fuels (incl. Carbon Capture) Geography Kernel functions Magnesium Mathematical Modeling and Industrial Mathematics Mineral Resources Natural streams Original Paper Parameters Particle swarm optimization Physics Regression analysis Root-mean-square errors Statistics for Engineering Support vector machines Surface water Sustainable Development Turbidity Water quality Water quality measurements Water resources Support vector regression Regression analysis Multi-kernel learning Natural water resources Water quality parameters Kernel learning
ISSN:	1520-7439, 1573-8981
Online Access:	Get full text
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