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

Area of Science:

• Environmental Science
• Data Science
• Hydrogeology

Background:

• Surface water quality monitoring is crucial for environmental health.
• Predicting dissolved oxygen (DO) levels is vital for aquatic ecosystems.
• Hydro-chemical data often exhibits complex nonlinear patterns.

Purpose of the Study:

• To develop and compare kernel function-based regression models for predicting surface water dissolved oxygen.
• To assess the capability of nonlinear models in capturing complex hydro-chemical dynamics.
• To evaluate the predictive and generalization performance of various kernel regression techniques.

Main Methods:

• Nonlinear feature selection was applied to the hydro-chemical dataset.
• BDS statistics were used to confirm data nonlinearity.
• Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using a Gaussian kernel.
• Cross-validation was employed for model parameter optimization.

Main Results:

• All developed kernel regression models successfully captured the nonlinear features of the hydro-chemical data.
• The models transformed data into a high-dimensional feature space using kernel functions.
• Performance metrics indicated comparable predictive and generalization abilities across all tested kernel-based methods.
• The constructed models demonstrated adequacy in fitting nonlinear data and strong predictive capabilities.

Conclusions:

• Kernel function-based regression models are effective tools for predicting dissolved oxygen in nonlinear hydro-chemical datasets.
• These methods offer robust solutions for surface water quality assessment and environmental monitoring.
• The study validates the utility of kernel methods in handling complex, nonlinear environmental data.