Multimodal Stacked Modeling for Simultaneous Detection of Nutrient Concentrations With Turbidity Correction

Abstract

In this paper, an innovative method for the simultaneous determination of nitrite, nitrate, and COD in water in the presence of turbidity as a source of noise in spectroscopic data has been investigated. UV–Vis absorption spectrometry and advanced machine learning are proposed to develop a stacking model, a sophisticated modeling approach that combines several basic models (PLS, Lasso, and Ridge regression) and a meta-regressor (Random Forest regressor) to improve prediction accuracy by incorporating baseline correction and principal component analysis (PCA) to mitigate the effects of turbidity on spectroscopic data. After applying these corrections, a significant improvement was observed: The root mean square error (RMSE) and the mean absolute error (MAE) were significantly reduced, and the correlation coefficient (R²) between predicted and actual values of nitrite, nitrate, COD, and turbidity was greater than 0.96, for all compounds in the test data set, that demonstrate the ability of the proposed stacking model to accurately predict nutrient concentrations simultaneously, even in complex environments; the proposed model may provide a valuable alternative to wet chemical methods. Due to its high accuracy and fast response, the proposed model can be used as an algorithm for the construction of nutrient sensors. This paper highlights the importance of integrating advanced modeling and data correction techniques to improve the robustness and accuracy of predictive models in environmental chemistry, thus providing valuable information for environmental monitoring and management.