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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.01465 |
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| _version_ | 1866918235585118208 |
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| author | Si, Hongnan Li, Tong Chen, Yujie Liao, Xin |
| author_facet | Si, Hongnan Li, Tong Chen, Yujie Liao, Xin |
| contents | Water quality monitoring is a core component of ecological environmental protection. However, due to sensor failure or other inevitable factors, data missing often exists in long-term monitoring, posing great challenges in water quality analysis. This paper proposes a Neural Tucker Convolutional Network (NTCN) model for water quality data imputation, which features the following key components: a) Encode different mode entities into respective embedding vectors, and construct a Tucker interaction tensor by outer product operations to capture the complex mode-wise feature interactions; b) Use 3D convolution to extract fine-grained spatiotemporal features from the interaction tensor. Experiments on three real-world water quality datasets show that the proposed NTCN model outperforms several state-of-the-art imputation models in terms of accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01465 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Tucker Convolutional Network for Water Quality Analysis Si, Hongnan Li, Tong Chen, Yujie Liao, Xin Machine Learning 68T07 (Primary) 62M10, 65C60 (Secondary) I.2.7 Water quality monitoring is a core component of ecological environmental protection. However, due to sensor failure or other inevitable factors, data missing often exists in long-term monitoring, posing great challenges in water quality analysis. This paper proposes a Neural Tucker Convolutional Network (NTCN) model for water quality data imputation, which features the following key components: a) Encode different mode entities into respective embedding vectors, and construct a Tucker interaction tensor by outer product operations to capture the complex mode-wise feature interactions; b) Use 3D convolution to extract fine-grained spatiotemporal features from the interaction tensor. Experiments on three real-world water quality datasets show that the proposed NTCN model outperforms several state-of-the-art imputation models in terms of accuracy. |
| title | Neural Tucker Convolutional Network for Water Quality Analysis |
| topic | Machine Learning 68T07 (Primary) 62M10, 65C60 (Secondary) I.2.7 |
| url | https://arxiv.org/abs/2512.01465 |