Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks

Autor: Juan Huan, Jialong Yuan, Hao Zhang, Xiangen Xu, Bing Shi, Yongchun Zheng, Xincheng Li, Chen Zhang, Qucheng Hu, Yixiong Fan, Jiapeng Lv, Liwan Zhou
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Water Science and Technology, Vol 89, Iss 8, Pp 1961-1980 (2024)
Druh dokumentu: article
ISSN: 0273-1223
1996-9732
DOI: 10.2166/wst.2024.122
Popis: Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore, there is an urgent need for a method that can accurately identify various types of agricultural organic pollution to prevent the water ecosystems in the region from significant organic pollution. In this study, a network model called RA-GoogLeNet is proposed for accurately identifying agricultural organic pollution in the river network area of the Jiangnan Plain. RA-GoogLeNet uses fluorescence spectral data of agricultural non-point source water quality in Changzhou Changdang Lake Basin, based on GoogLeNet architecture, and adds an efficient channel attention (ECA) mechanism to its A-Inception module, which enables the model to automatically learn the importance of independent channel features. ResNet are used to connect each A-Reception module. The experimental results show that RA-GoogLeNet performs well in fluorescence spectral classification of water quality, with an accuracy of 96.3%, which is 1.2% higher than the baseline model, and has good recall and F1 score. This study provides powerful technical support for the traceability of agricultural organic pollution. HIGHLIGHTS The proposed RA-GoogLeNet is time-efficient, accurate, and superior to other convolutional neural network models with fewer parameters than traditional methods.; ECA attention mechanism and ResNet were added to improve the model's ability to identify agricultural non-point source pollution in the study area.; The Leaky ReLU activation function used in this model can extract more image features.;
Databáze: Directory of Open Access Journals
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