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Huan J, Yuan J, Zhang H, Xu X, Shi B, Zheng Y, Li X, Zhang C, Hu Q, Fan Y, Lv J, Zhou L. Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks. Water Sci Technol 2024; 89:1961-1980. [PMID: 38678402 DOI: 10.2166/wst.2024.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 04/02/2024] [Indexed: 04/30/2024]
Abstract
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.
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Affiliation(s)
- Juan Huan
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China E-mail:
| | - Jialong Yuan
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Hao Zhang
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Xiangen Xu
- Changzhou Environmental Science Research Institute, Changzhou 213002, China
| | - Bing Shi
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Yongchun Zheng
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Xincheng Li
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Chen Zhang
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Qucheng Hu
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Yixiong Fan
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Jiapeng Lv
- School of Computer and Artificial Intelligence, School of Alibaba Cloud Big Data, School of Software, Changzhou University, Changzhou 213100, China
| | - Liwan Zhou
- Changzhou Environmental Science Research Institute, Changzhou 213002, China
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