中国环境科学

作者：董杨, 崔厚欣, 邓家春, 马俊杰, 秦海啸

单位：1. 防灾科技学院, 河北 三河市 065201;2. 河北先河环保科技股份有限公司, 河北 石家庄 050035

关键词：高光谱成像,光谱分析,连续小波变换,溢油厚度,定量监测

基金：国家重点研发计划(2023YFE0102400);河北省高等教育教学改革研究与实践项目(2022GJJG490)

Focusing on the monitoring of 0# diesel oil film thickness, the primary objective is to meticulously analyze the spectral curve characteristics of different thickness oil films. Secondly, to delve deeper into the intricate relationship between spectral data and oil film thickness, the Morlet multi-scale continuous wavelet transform (CWT) technique is introduced. This enables the precise identification of spectral bands that are highly sensitive to oil film thickness, effectively addressing the challenge posed by the high dimensionality and complexity of hyperspectral data. Consequently, this approach significantly enhances the accuracy of thickness regression predictions. At the same time, the CatBoost regression model, with its efficient computing performance, strong feature capture ability, and excellent generalization ability, efficiently integrates these sensitive features and constructs a precise regression prediction model of the oil film thickness, accelerating the real-time monitoring speed of oil spill events, thereby achieving the immediate capture of changes in the thickness of the oil film and ensuring the accuracy of the prediction results, providing a solid scientific basis and technical support for the rapid initiation of oil spill emergency responses and the formulation of precise prevention and control strategies. The results show that the multi-scale continuous wavelet transform technology plays a key role in this study. It can effectively extract the sensitive bands highly related to the thickness of the oil film from the massive hyperspectral data, thereby significantly improving the accuracy and efficiency of oil spill thickness monitoring. The CatBoost regression model can better capture the change category characteristic data of the oil film thickness, further enhancing the generalization ability and robustness of the model. The diesel oil film thickness prediction model established by the CatBoost regression model shows extremely high accuracy, with R2=0.90, RMSE=95.14μm,δ=30.126% on the validation set.

来源：2024年第11期

《中国环境科学》期刊编辑部

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