# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 4 Conclusions  
This article proposes a novel oil spill spectral analysis method that simultaneously identifies oil types and predicts oil film thickness. The method integrates a self-designed detection and environmental simulation system, which significantly improves the reliability of the dataset and the practicality of the model. Furthermore, the proposed lightweight multi-task unified framework, the Wavelet CNN-sLSTM-KET framework, demonstrates the ability to handle both classification and regression tasks using fluorescence spectra without relying on fully connected layers. The framework achieves high accuracy and robustness in classification tasks and effectively predicts oil film thicknesses across a range of 50 \(\upmu\)m to 0.5 mm, achieving strong R\({}^{2}\) and RPD values and low RMSE values, all without requiring preprocessing. Moreover, thanks to its well-conceived architecture and innovative feature  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Accuracy** & **R\({}^{2}\)** & **RPD** & **Iteration** &
\begin{tabular}{c} **Noise** \\ **Sensitivity** \\ \end{tabular} & **Parameter** \\ \hline Hybrid & 99.76\% & 0.9806 & 7.1876 & 400 & 16.48\% & 4.57 M \\ Framework & 99.52\% & 0.9392 & 4.0542 & 1000 & 66.34\% & 16.79 M \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of Hybrid framework and transformer.  
extraction modules, the model's performance decreases by only 16% under high-noise conditions, indicating excellent robustness. These characteristics establish the proposed framework as a foundational solution for fast, general, and precise oil spill spectral analysis, driving advancements in non-destructive oil spill detection technologies. Its scalability, real-time capability, and lightweight design overcome the limitations of traditional oil spill analysis models, making it highly suitable for practical applications. Future research will aim to expand the representativeness of the dataset to further improve the model's generalizability and robustness. The development of miniaturized spectrometer devices could broaden the framework's application scenarios, enabling its use in various fields, such as industrial quality control and remote sensing. Additionally, incorporating advanced learning strategies like transfer learning and reinforcement learning could enhance the model's adaptability to diverse datasets, further solidifying its role as a versatile tool for oil spill analysis.  
Conceptualization, S.Z. and M.L.; methodology, software, and writing--original draft, S.Z.; writing--review and editing, S.Z., M.L., and J.L.; investigation, J.L.; funding acquisition, J.L.; supervision, J.L.; validation, J.L. 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, grant number 60578047, and the Natural Science Foundation of Shanghai, grant numbers 17 ZR1402200 and 13 ZR1402600.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors thank [PERSON] and [PERSON] for effective backup.  
The authors declare no conflicts of interest.