# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 3 Results and Discussion
### Model Performance Evaluation
#### 3.3.2 Regression Performance
This suggests that the transformer's global attention weights are significantly disrupted by noise, impairing effective feature extraction. Overall, these robustness tests indicate that the designed WaveConv block significantly enhances the model's noise resilience. Its multi-scale convolutional kernels and residual connections create a synergistic path for local denoising and global fitting, achieving a balanced improvement in noise robustness and multi-task performance. This advancement further enhances the practicality of LIF technology. Table 7 summarizes the key metrics, stable training epochs, noise sensitivity, and parameter counts for both the Hybrid framework and the transformer model. Here, noise sensitivity is defined as the average reduction in accuracy and R\({}^{2}\) at an SNR of 15 dB relative \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **SNR (dB)** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **MAE** & **RMSE** & **R\({}^{2}\)** & **RPD** \\ \hline \multirow{4}{*}{Hybrid Framework} & GT & 0.9976 & 0.9973 & 0.9976 & 0.9975 & 0.0068 & 0.0212 & 0.9806 & 7.1876 \\  & 15 & 0.8357 & 0.8475 & 0.8397 & 0.8402 & 0.0365 & 0.0651 & 0.8166 & 2.3353 \\  & 20 & 0.9405 & 0.9427 & 0.9435 & 0.9428 & 0.0256 & 0.0489 & 0.8965 & 3.1079 \\  & 25 & 0.9786 & 0.9797 & 0.9801 & 0.9799 & 0.0161 & 0.0343 & 0.9492 & 4.4371 \\  & 30 & 0.9952 & 0.9949 & 0.9954 & 0.9951 & 0.0081 & 0.0234 & 0.9763 & 6.5 \\ \hline \multirow{4}{*}{Transformer} & GT & 0.9952 & 0.995 & 0.9953 & 0.9951 & 0.0266 & 0.0375 & 0.9392 & 4.0542 \\  & 15 & 0.5488 & 0.5734 & 0.5472 & 0.5387 & 0.1105 & 0.1431 & 0.1141 & 1.0625 \\ \cline{1-1}  & 20 & 0.7417 & 0.7544 & 0.7443 & 0.7409 & 0.0852 & 0.1144 & 0.4345 & 1.3298 \\ \cline{1-1}  & 25 & 0.9202 & 0.9212 & 0.9225 & 0.9212 & 0.0567 & 0.0797 & 0.7251 & 1.9072 \\ \cline{1-1}  & 30 & 0.9714 & 0.9721 & 0.9719 & 0.9719 & 0.0387 & 0.0541 & 0.8736 & 2.8124 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of robustness testing for Hybrid framework and transformer. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Raw** & & **SG** & & **SG + SNV** & **Normalization** \\ \hline \multirow{4}{*}{Hybrid Framework} & MAE & 0.0068 & MAE & 0.0090 & MAE & 0.0075 & MAE & 0.0076 \\  & RMSE & 0.0212 & RMSE & 0.0241 & RMSE & 0.0229 & RMSE & 0.0209 \\  & R\({}^{2}\) & 0.9806 & R\({}^{2}\) & 0.9750 & R\({}^{2}\) & 0.9774 & R\({}^{2}\) & 0.9810 \\  & RPD & 7.1876 & RPD & 6.3188 & RPD & 6.6544 & RPD & 7.2614 \\ \hline \multirow{4}{*}{Transformer} & MAE & 0.0266 & MAE & 0.0250 & MAE & 0.0241 & MAE & 0.0251 \\  & RMSE & 0.0375 & RMSE & 0.0349 & RMSE & 0.0358 & RMSE & 0.0355 \\  & R\({}^{2}\) & 0.9392 & R\({}^{2}\) & 0.9473 & R\({}^{2}\) & 0.9446 & R\({}^{2}\) & 0.9455 \\  & RPD & 4.0542 & RPD & 4.3567 & RPD & 4.2505 & RPD & 4.2816 \\ \hline \multirow{4}{*}{SVC} & MAE & 0.4000 & MAE & 0.4059 & MAE & 0.4264 & MAE & 0.4058 \\  & RMSE & 0.542 & RMSE & 0.5490 & RMSE & 0.5679 & RMSE & 0.5499 \\  & R\({}^{2}\) & 0.7028 & R\({}^{2}\) & 0.6962 & R\({}^{2}\) & 0.6737 & R\({}^{2}\) & 0.694 \\  & RPD & 1.8343 & RPD & 1.8109 & RPD & 1.7507 & RPD & 1.8079 \\ \hline \multirow{4}{*}{LR} & MAE & 0.5446 & MAE & 0.5446 & MAE & 0.5498 & MAE & 0.5459 \\  & RMSE & 0.6639 & RMSE & 0.6639 & RMSE & 0.6754 & RMSE & 0.6661 \\ \cline{1-1}  & R\({}^{2}\) & 0.5541 & R\({}^{2}\) & 0.5541 & R\({}^{2}\) & 0.5385 & R\({}^{2}\) & 0.5511 \\ \cline{1-1}  & RPD & 1.4976 & RPD & 1.4976 & RPD & 1.472 & RPD & 1.4925 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test set regression evaluation index results. to the baseline. The results show that while both models perform similarly in classification tasks, the proposed framework significantly outperforms the transformer model in regression tasks. Owing to the multi-scale feature extraction capability of the WaveConv module and the nonlinear mapping ability of KAN, the Hybrid framework requires fewer training epochs and exhibits enhanced robustness.