# Quantitative Analysis of Forest Water COD Value Based on UV-vis and FLU Spectral Information Fusion
## 5 Conclusions  
In summary, we have demonstrated COD prediction for real forest water samples based on UV-vis and FLU spectroscopy. Due to the large amounts of spectral information, high data dimensionality, and noise interference, the SPA and CARS algorithms are introduced in the feature-selection process. By comparison, the model selected based on CARS has higher accuracy, less error, and higher stability than the SPA model. However, the RMSE values of the two single-spectrum models both exceed \(1\), which cannot meet  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**UV–vis**} & \multicolumn{2}{c}{**FLU**} & \multicolumn{2}{c}{**Feature-Level Fusion**} & \multicolumn{2}{c}{**Decision-Level Fusion**} \\ \cline{2-9}  & **CARS** & **SPA** & **CARS** & **SPA** & **CARS** & **SPA** & **ENTROPY TOPSIS** & **RF** \\ \hline RMSE (mg/L) & 1.3698 & 2.2109 & 1.2909 & 2.3423 & 0.7886 & 0.9079 & 0.4582 & 0.4629 \\ \(R^{2}\) & 0.9466 & 0.9318 & 0.9680 & 0.9068 & 0.9830 & 0.9754 & 0.9932 & 0.9931 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the predictive results obtained by single-spectrum models, feature-level fusion models, and decision-level fusion models, respectively.  
Figure 5: Fitting results between predicted values and actual values. (**a**) TOPSIS and (**b**) RF voting mechanism for decision-level data fusion.  
the accuracy requirements. Considering the deficiencies of the existing UV-vis and FLU spectrum models that cannot be well resolved by soft compensation, we use feature-level and decision-level data fusion methods to further optimize the predictive model. After feature-level fusion, the RMSE and \(R^{2}\) values of the model can be optimized to 0.7886 mg/L and 0.9830. Accordingly, with the decision-level data fusion, the RMSE value has been further reduced to 0.4582 mg/L, which is 64.51% and 41.90% lower than those of the single-spectrum and feature-level fusion models, respectively. At the same time, \(R^{2}\) is also increased to 0.9932. In forest water COD detection, the decision-level data fusion model based on two voting mechanisms greatly improves prediction stability and reliability of UV-vis and FLU spectroscopy. Our research lays the foundations for monitoring the pollution of water bodies around forest lands and assessing the impact of human activities on forest ecosystems. Thus, it makes it easier to take effective prediction and management measures to promote the sustainable development of forest protection.  
Conceptualization, C.L., X.M. and L.J.; methodology, C.L.; software, X.M.; validation, C.L., X.M. and Y.T.; formal analysis, C.L., X.M., S.L., Y.J. and J.D.; investigation, C.L. and X.M.; resources, C.L. and L.J.; data curation, C.L. and X.M.; writing--original draft preparation, C.L. and X.M.; writing--review and editing, C.L. and L.J.; visualization, C.L.; supervision, C.L. and L.J.; project administration, C.L. and L.J.; funding acquisition, C.L. and L.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) (nos. 12273012, 62001235).  
The data from the current study are available from the corresponding author upon reasonable request.  
We would like to thank the editors and reviewers for their valuable opinions and suggestions that improved this study.  
The authors declare no conflict of interest.