title: Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data.
abstract: Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.
authors: Academy of Scientific and Innovative Research, Anusandhan Bhawan, Rafi Marg, New Delhi, 110001, India, kpsingh_52@yahoo.com.
authors: Kunwar P
authors: KP
authors: Singh
authors: Shikha
authors: S
authors: Gupta
authors: Premanjali
authors: P
authors: Rai
mesh_terms: D000465:Algorithms; D057919:Biological Oxygen Demand Analysis; D001699:Biometry; D004784:Environmental Monitoring / Q000379:methods; D005618:Fresh Water / Q000737:chemistry; D016018:Least-Squares Analysis; D015233:Models, Statistical; D016011:Normal Distribution; D010100:Oxygen / Q000032:analysis*; D012044:Regression Analysis
chemical_list: D010100:Oxygen