# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 1 Introduction
Therefore, this study simulates real-world environments by designing dynamic data collection scenarios and using varied laser power levels to enhance data diversity and representativeness. Second, existing analysis methods have significant limitations. Most oil spill identification models primarily focus on improving accuracy with traditional or deep learning algorithms. However, given the complex interference from multiple sources in field conditions and the nonlinear variations in oil film thickness due to wind and waves, it is essential to enhance the robustness against interference and the nonlinear modeling capabilities of current methods. Finally, there is a lack of unified models. Current approaches focus on either oil-type identification or thickness prediction but rarely both. The variations in oil film fluorescence spectra depend not only on film thickness but also on oil type and laser power, which affect spectral distribution and peak positions [24; 29; 30]. In addition, traditional thickness prediction methods have inherent limitations. Fluorescence-based methods require real-time calibration due to sensitivity to the detection distance, while Raman suppression methods fail with thicker films, as the seawater signal is obscured [31]. [PERSON] et al. [25] proposed a fluorescence-to-Raman ratio method to address this limitation, but its accuracy depends on the detection angle. [PERSON] et al. [23] further extended the detection range using fluorescence radiative transfer mechanisms but noted that validation is limited to only one or two oil types. Given that thickness correlations may vary across different oils, a general model capable of learning the spectral features of various oil types is urgently needed. Therefore, designing a multifunctional analysis model that integrates oil-type identification and thickness prediction is essential. Such a unified multi-task framework would improve prediction accuracy and generalizability while reducing parameter count, thereby enabling a fast, online analysis of resource-constrained devices. Existing oil spill spectral analysis methods include traditional chemometric approaches, machine learning techniques, and deep learning technologies. Traditional methods, such as principal component analysis (PCA) [32] and partial least squares regression (PLSR) [33], rely on capturing nonlinear relationships between variables for identification and prediction. While effective in certain applications, these methods are insufficient for oil spill analysis tasks involving multiple influencing factors. Machine learning techniques, including support vector machines (SVMs), k-nearest neighbors (KNNs), and logistic regression (LR), have been combined with chemometric methods to improve performance [19; 34]. However, their scalability, computational efficiency, and reliance on preprocessing limit their effectiveness in online and complex environments [26; 35]. Deep learning, with its ability to learn complex patterns from large datasets, has been increasingly applied to oil spill spectral analysis [36; 37]. Convolutional neural networks (CNNs), in particular, have gained attention for their strong feature extraction and data compression capabilities. By independently focusing on key regions, CNNs can improve accuracy. However, for spectral data with sequential feature correlations, CNNs alone may fail to capture these dependencies, which reduces their robustness in complex scenarios. Moreover, due to their fully connected layers, CNNs typically have a large number of parameters, making them difficult to be deployed on devices with limited hardware resources for real-time analysis. Therefore, it is necessary to improve existing deep learning models, so that they are better suited for spectral analysis tasks and offer enhanced robustness, nonlinear modeling capabilities, and deployability. To address these issues, this study proposes a novel oil spill spectral analysis method that simultaneously performs oil-type identification and thickness prediction.