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Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data

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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.

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Acknowledgements

The authors thank the Director, CSIR-Indian Institute of Toxicology Research, Lucknow, for his keen interest in this work.

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Authors and Affiliations

  1. Academy of Scientific and Innovative Research, Anusandhan Bhawan, Rafi Marg, New Delhi, 110001, India

    Kunwar P. Singh, Shikha Gupta & Premanjali Rai

  2. Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research (Council of Scientific and Industrial Research), Post Box 80, Mahatma Gandhi Marg, Lucknow, 226 001, India

    Kunwar P. Singh, Shikha Gupta & Premanjali Rai

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Correspondence to Kunwar P. Singh.

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Singh, K.P., Gupta, S. & Rai, P. Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data. Environ Monit Assess 186, 2749–2765 (2014). https://doi.org/10.1007/s10661-013-3576-6

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