人工智能
计算机科学
卷积神经网络
均方误差
模式识别(心理学)
生物系统
数学
统计
生物
作者
Yu Si,Dandan Zhu,Ying Chen,Junfei Liu,Ting Chen,Zhiyang Dong
标识
DOI:10.1088/1361-6501/acd8e2
摘要
Abstract The frequent occurrence of algal blooms has seriously affected the marine environment and human production activities. Therefore, it is crucial to monitor the phytoplankton concentration in water bodies. In this study, a prediction method for brown tide algae using improved Gramian angular field (IGAF) and deep learning based on the laser-induced fluorescence spectrum was proposed. The method combined one-dimensional (1D) fluorescence spectrum with IGAF for image coding. The internal normalizing approach of the original Gramian angle field algorithm was upgraded from local to global, which can increase the difference between samples with various concentrations. Then, we established a novel technique that fully takes into account the Gramian angular difference field and Gramian angular summation field features, allowing it to control the main and sub-diagonal features and successfully convert 1D sequences into images by adding various weight factors. Using depthwise separable convolutional neural network to extract image features helps reduce model training parameters, paired with long short-term memory network to rapidly predict the concentration of brown tide. To confirm the actual performance of the given approach, ablation and contrast experiments were carried out, and the results showed that the method’s regression accuracy, R 2 was 97.8%, with the lowest mean square error and mean absolute error. This study investigated the transformation of 1D spectra into images using IGAF, which not only explored the application of the fluorescence spectrum image coding method for algal regression but also enabled the introduction of the potent benefits of deep learning image processing into the field of spectral analysis.
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