Quantitative Analysis of Forest Water COD Value Based on UV–vis and FLU Spectral Information Fusion

Abstract

As an important ecosystem on the earth, forests not only provide habitat and food for organisms but also play an important role in regulating environmental elements such as water, atmosphere, and soil. The quality of forest waters directly affects the health and stability of aquatic ecosystems. Chemical oxygen demand (COD) is commonly used to assess the concentration of organic matter and the pollution status of water bodies, which is helpful in assessing the impact of human activities on forest ecosystems. To effectively measure the COD value, water samples were prepared from Purple Mountain in Nanjing and nearby rivers and lakes. Using ultraviolet–visible (UV–vis) and fluorescence (FLU) spectroscopy combined with data fusion, the COD values of the forest water were accurately measured. Due to the large dimensionality of spectral data, the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were applied to the selection of characteristic wavelengths. By establishing a discriminant model for single-level data and using the voting mechanism to fuse the output results of different models, a relatively high determination coefficient (R2) of 0.9932 and a low root-mean-square error (RMSE) of 0.4582 were obtained based on the decision-level data fusion model. Compared with the single-spectrum and feature-level fusion models, the decision-level fusion scheme achieves an efficient, comprehensive, and accurate quantification of the water COD value. This study has important applications in forest protection, water resources management, sewage treatment, and the food processing field.