# 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
to unseen data. In comparison, the transformer model's RPD values are consistently below 5, with a lower RMSE and R\({}^{2}\), which aligns with its overall performance in Table 4. On the other hand, the results in Figure 11 highlight the importance of verifying the generalization capability of oil spill analysis models during development. Due to variations in intrinsic properties and external disturbances, thickness prediction performance may differ across oil types. This result further demonstrates that the proposed method is versatile, capable of learning individual spectral features for different oils. With continued data augmentation, the model has the potential to become a universal framework for oil spill thickness analysis. Similar to Section 3.3.1, Table 5 summarizes the regression performance of different methods under various preprocessing conditions. From Table 5, it can be observed that preprocessing does not significantly improve regression performance across the models. Instead, similar to the classification task, performance fluctuations are noted. Given the high complexity of the dataset, strong nonlinear capabilities are required, and preprocessing cannot enhance the nonlinearity of the models. The proposed Hybrid framework outperforms other models significantly, even without preprocessing. While slight performance fluctuations occur after SG and SNV preprocessing, the RMSE remains around 0.2, indicating that additional interference signals, introduced by preprocessing, do not cause significant prediction deviations. Moreover, the RPD and R\({}^{2}\) values remain high, confirming the model's reliability. The transformer model shows an improvement in regression performance after preprocessing, suggesting that preprocessing reduces noise interference in the data, partially addressing the transformer's sensitivity to noise. However, the performance gain is limited, which is related to the transformer's constrained nonlinear mapping capability. This further highlights the necessity of improving the transformer model using the KAN method. To account for the strong noise interference in marine environments, noise signals with SNR of 15 dB, 20 dB, 25 dB, and 30 dB were added to the dataset to evaluate the noise robustness of the proposed Hybrid framework. Table 6 summarizes the classification and regression performance of both the Hybrid framework and the transformer model under these different noise conditions. In classification tasks, as noise levels increase, the Hybrid framework demonstrates notable robustness. At an SNR of 15 dB, its accuracy is 0.8357, a reduction of only about 16% compared to the noise-free case. As noise decreases, the classification performance recovers rapidly; at 30 dB, the model achieves an accuracy of 0.9952, nearly identical to the noise-free scenario, indicating strong noise adaptability. Figure 11: (**a–e**) Scatter plots of regression predictions by the Hybrid framework for Shell HELX 0W-40, Shell HELX 5W-20, Mobil Super 4T 20W-50, Mobil Super 1000 Xi Diesel 15W-40, and Mobil 2000 5W-40, respectively; (**f–j**) scatter plots of regression predictions by the transformer model. For regression tasks, the proposed model also exhibits strong resilience. Although at 15 dB, the MAE and RMSE increase significantly and regression performance degrades compared to the noise-free condition, the R\({}^{2}\) value remains above 0.8, and the RPD exceeds 2, indicating that the model's regression predictions are still meaningfully correlated. As noise diminishes, the regression performance gradually recovers, reaching levels comparable to the noise-free state at 30 dB. In contrast, the transformer model is severely affected by noise. In classification tasks at 15 dB, its accuracy drops sharply to 0.5488, a decline of roughly 41% compared to the noise-free condition. Its regression performance also deteriorates dramatically, with an R\({}^{2}\) of only 0.1141 and an RPD of 1.0625 at 15 dB, which is close to random prediction.