ArticleMicromachines2023
A Method of Water COD Retrieval Based on 1D CNN and 2D Gabor Transform for Absorption-Fluorescence Spectra.
Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Water chemical oxygen demand prediction based on a one-dimensional multi-scale feature fusion convolutional neural network and ultraviolet-visible spectroscopy.RSC advances · 2025Article
- A Transfer Learning-Based VGG-16 Model for COD Detection in UV-Vis Spectroscopy.Journal of imaging · 2025Article
- Prediction of Water Chemical Oxygen Demand with Multi-Scale One-Dimensional Convolutional Neural Network Fusion and Ultraviolet-Visible Spectroscopy.Biomimetics (Basel, Switzerland) · 2025Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Chemical Oxygen Demand (COD) is one of the indicators of organic pollution in water bodies. The rapid and accurate detection of COD is of great significance to environmental protection. To address the problem of COD retrieval errors in the absorption spectrum method for fluorescent organic matter solutions, a rapid synchronous COD retrieval method for the absorption-fluorescence spectrum is proposed. Based on a one-dimensional convolutional neural network and 2D Gabor transform, an absorption-fluorescence spectrum fusion neural network algorithm is developed to improve the accuracy of water COD retrieval. Results show that the RRMSEP of the absorption-fluorescence COD retrieval method is 0.32% in amino acid aqueous solution, which is 84% lower than that of the single absorption spectrum method. The accuracy of COD retrieval is 98%, which is 15.3% higher than that of the single absorption spectrum method. The test results on the actual sampled water spectral dataset demonstrate that the fusion network outperformed the absorption spectrum CNN network in measuring COD accuracy, with the RRMSEP improving from 5.09% to 1.15%.
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Registered trials
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