基于三维荧光光谱和GBDT-LR的褐潮藻辨识

Identification of Brown Tide Algae Based on Three-Dimensional Fluorescence Spectra and GBDT-LR

近年来频繁发生的褐潮污染给沿海地区经济带来巨大损失。准确、高效地识别褐潮藻对预防海洋环境污染意义重大。采用三维荧光光谱、梯度提升决策树(GBDT)和逻辑回归(LR)相结合的方法,实现了对褐潮藻的准确辨识。为解决LR模型对非线性数据的特征组合能力较弱的问题,引入GBDT算法,充分利用集成学习算法在处理非线性数据上的优势。将GBDT的预测结果作为新特征代替原来的特征输入LR模型,建立了一种将GBDT与LR相融合的褐潮藻辨识模型(GBDT-LR)。针对复杂海洋环境中其他门类藻的干扰,实验引入小球藻、细长聚球藻等5种不同门类的海藻作为对比,并对处于不同生长周期的褐潮藻辨识情况进行分析。相同条件下通过将所提模型与LR、支持向量机(SVM)和反向传播(BP)神经网络等模型进行对比。结果表明,GBDT-LR在分类准确率、召回率和F1分数等评价指标上均优于其他模型,处于指数生长期的藻类荧光光谱最为稳定,这一时期的褐潮藻辨识结果最好。

The frequent occurrence of brown tide pollution in recent years has brought huge losses to the economy of coastal areas.Therefore,the accurate and efficient identification of brown tide algae is of great significance to the prevention of marine environmental pollution.In this paper,a combination method of three-dimensional fluorescence spectroscopy,gradient boosting decision tree(GBDT),and logistic regression(LR)is used to achieve accurate identification of brown tide algae.In order to solve the problem of weak feature combination ability of LR model for nonlinear data,the GBDT algorithm is introduced to make full use of the advantages of the integrated learning algorithm in processing nonlinear data.The prediction result of GBDT model is used as a new feature instead of the original feature which is input into the LR model,and a brown tide algae recognition model(GBDT-LR)that combines GBDT and LR is established.In response to the interference of other types of algae in the complex marine environment,five different types of algae such as Chlorella and Synechococcus elongatus are introduced for comparison in the experiment,and analyzed the identification of brown tide algae in different growth cycles are analyzed.The proposed model is compared with LR,support vector machine(SVM)and back propagation(BP)neural network under the same conditions.The results show that the GBDT-LR model is superior to the other models in terms of classification accuracy,recall rate,and F1-score.The fluorescence spectrum of algae in the exponential growth period is the most stable,and the identification result of the brown tide algae in this period is the best.