An Improved-Bagging Model for Water Chemical Oxygen Demand Measurements Using UV-Vis Spectroscopy

Abstract

The ultraviolet-visible (UV-Vis) spectroscopy measurement method of Chemical Oxygen Demand (COD) in water is a simple physical method that can measure water without secondary pollution from chemical reagents. To solve the problems of low accuracy and insufficient generalization capability of the COD prediction model, an improved Bagging algorithm is proposed and evaluated in this study. The Improved-Bagging algorithm can reduce model variance and bias concurrently, and improves the accuracy and stability of the traditional Bagging algorithm. Results show that the Improved-Bagging algorithm achieves a better prediction ability on different preprocessed data than the traditional Bagging algorithm. After ensemble empirical mode decomposition based (EEMD-Based) algorithm denoising and stability competitive adaptive reweighted sampling (SCARS) algorithm dimension reduction, Improved-Bagging model achieves the best prediction performance. Its coefficient of determination (R2) on the prediction set reached 0.9317, its root mean square error of prediction (RMSEP) reached 5.39 mg/L, and its variance reached 5.53 mg2. Results also show that the Improved-Bagging algorithm can accurately measure the COD concentration in water, which lays the foundation for the wide application of spectroscopy to measure water quality parameters.