This study developed an intelligent method for identifying and quantifying water pollution sources in estuarine areas. It characterized the excitation-emission matrix (EEM) fluorescence spectra from seven end-members, including seawater, rainwater, and five pollution sources typical of these areas. A deep learning model was established to identify and quantify these pollution sources in mixed water bodies. The model was fed either the original EEM or a combined EEM and gradient input. The results indicated that the combined input enhanced classification and quantification accuracy; Although model accuracy declined with an increasing number of mixed pollution sources, the combined input still improved classification accuracy by 3.1 % to 6.8 %; When the proportion of rainwater and seawater was below 70 %, the model maintained a classification accuracy of 57.4 % with original input and 61.3 % with combined input, with root mean square error values for the pollution source proportion being 12.2 % and 11.4 %, respectively.

中文翻译：

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使用荧光光谱和基于梯度的深度学习识别和量化河口中的多个污染源


本研究开发了一种识别和量化河口地区水污染源的智能方法。它表征了来自七个末端元的激发-发射矩阵 （EEM） 荧光光谱，包括海水、雨水和这些区域典型的五个污染源。建立了一个深度学习模型来识别和量化混合水体中的这些污染源。该模型被馈送到原始 EEM 或组合的 EEM 和梯度输入。结果表明，组合输入提高了分类和量化的准确性;尽管模型精度随着混合污染源数量的增加而下降，但综合输入仍将分类精度提高了 3.1 % 至 6.8 %;当雨水和海水比例低于 70 % 时，模型保持了原始输入的 57.4 % 和 61.3 % 的组合输入分类精度，污染源比例的均方根误差值分别为 12.2 % 和 11.4 %。