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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.