# Quantitative Analysis of Forest Water COD Value Based on UV-vis and FLU Spectral Information Fusion
## 4 Results and Analysis
### Analysis and Comparison of Modeling Results  
Using the LSSVM method, we establish corresponding predictive models based on the feature selection of UV-vis and FLU spectral data for analysis and comparison. During the modeling process, 80% of the samples are assigned to the training set and the remaining 20% are assigned to the predictive set. In the UV-vis single-spectrum model, the values of \(R^{2}\) and RMSE obtained by CARS feature selection are 0.9466 and 1.3698, respectively, while in the SPA model, they are 0.9318 and 2.2109. Correspondingly, in the FLU single-spectrum model, the \(R^{2}\) and RMSE of the CARS model are 0.9680 and 1.2909, and those of the SPA model are 0.9068 and 2.3423. After feature selection, the predictive accuracies of the two single-spectrum models are still relatively low. Figure 3: Results of feature selection for FLU spectra. (**a**) Selected feature wavelengths (spherical symbols) based on the SPA algorithm. (**b**) The number of selected wavelengths and (**c**) the RMSEV values varied with the iterations based on the CARS algorithm. Figure 2: Results of feature selection for UV–vis spectra. (**a**) Selected feature wavelengths (spherical symbols) based on the SPA algorithm. (**b**) The number of selected wavelengths and (**c**) the RMSEV values varied with the iterations based on the CARS algorithm. To avoid the limitations of the single-spectrum model in the analysis of complex water bodies, we combine UV-vis and FLU spectra through information fusion technology. Based on SPA and CARS feature selection, we de-quantified the two spectral datasets to construct new feature matrices [38; 39]. After feature-level data fusion, the important information results contained in the UV-vis and FLU spectra are combined. We make a linear fit between the obtained predicted results and the real COD values, as shown in Figure 4a,b. Compared with the single-spectrum model, the determination coefficient \(R^{2}\) and RMSE of the models have been significantly improved after data fusion. For SPA feature selection, the \(R^{2}\) and RMSE can reach 0.9754 and 0.9079, respectively. Correspondingly, the \(R^{2}\) and RMSE are 0.9830 and 0.7886 with the CARS algorithm. By contrast, although they are all feature-level data fusions, the linear fitting effect based on the CARS algorithm is better than that of the SPA model. The data after feature selection removes most of the redundant information from the original data and reduces the data dimensionality. Feature-level data fusion effectively retains spectral data while removing interfering information, achieving better predictive results. To further improve the predictive accuracy of the model, we optimize the two spectral models at the decision level. Based on the feature-level fusion models of CARS and SPA, we label the results as \(y_{CARS}\) and \(y_{SPA}\), respectively. As a comparison, we adopt the voting mechanism of the TOPSIS and RF algorithms for decision-level data fusion [40; 41]. For the TOPSIS algorithm, we combine the entropy weight with the optimal and inferior distances of \(D_{i}^{+}\) and \(D_{i}^{-}\) to obtain the composite score and weight of each indicator. The final weight coefficients assigned to the CARS and SPA feature-selection-based fusion models are 0.5279 and 0.4721, respectively. Based on TOPSIS, the results of decision-level data fusion can be expressed as  
\[y_{pred(TOPSIS)}=0.5279\times y_{CARS}+0.4721\times y_{SPA} \tag{7}\]  
For the RF algorithm, we can evaluate each indicator by calculating the Gini index of each feature to determine the corresponding weight [42]. The final weight coefficients assigned to the CARS and SPA feature-selection-based fusion models are 0.5031 and 0.4969, respectively. Based on RF, the results of decision-level data fusion can be expressed as  
\[y_{pred(RF)}=0.5031\times y_{CARS}+0.4969\times y_{SPA} \tag{8}\]  
The final predictive results obtained by the two voting mechanisms are shown in Figure 5a,b.