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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Veröffentlicht in:Natural resources research (New York, N.Y.) Jg. 30; H. 5; S. 3761 - 3775
Hauptverfasser: Najafzadeh, Mohammad, Niazmardi, Saeid
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.10.2021
Springer Nature B.V
Schlagworte:
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
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Zusammenfassung: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-measure parameters from those that can be measured easily. To this end, we proposed a novel modification of support vector regression (SVR), known as multiple-kernel support vector regression (MKSVR) algorithm. The MKSVR learns an optimal data representation for regression analysis by either linear or nonlinear combination of some precomputed kernels. For solving the optimization problem of the MKSVR, the particle swarm optimization (PSO) algorithm was used. The proposed algorithm was assessed using WQPs taken from Karun River, Iran. MKSVR was used to estimate chemical oxygen demand (COD) and biochemical oxygen demand (BOD) using nine WQPs as the input variables, namely electrical conductivity, sodium, calcium, magnesium, phosphate, nitrite, nitrate nitrogen, turbidity, and pH. The results of the proposed MKSVR were compared with those obtained using the SVR and Random Forest regression (RFR). The results showed that the MKSVR algorithm (correlation coefficient [R] = 0.8 and root mean squared error [RMSE] = 4.76 mg/l) increased the accuracy level of BOD prediction when compared with SVR (R = 0.68 and RMSE = 5.15 mg/l) and RFR (R = 0.77 and RMSE = 5.15 mg/l). In the case of COD estimation, the performance of a developed support vector machine (SVM) technique was satisfying. Overall, the use of MKSVR along with the PSO algorithm could demonstrate the superiority of the newly developed SVM technique for the WQPs estimation in the natural streams.
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ISSN:1520-7439
1573-8981
DOI:10.1007/s11053-021-09895-5