Neural Tucker Convolutional Network for Water Quality Analysis

• URL: http://arxiv.org/abs/2512.01465v2
• Date: Sun, 07 Dec 2025 13:18:38 GMT
• Title: Neural Tucker Convolutional Network for Water Quality Analysis
• Authors: Hongnan Si, Tong Li, Yujie Chen, Xin Liao,
• Abstract summary: This paper proposes a Neural Tucker Convolutional Network (NTCN) model for water quality data imputation.<n> 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.
• Score: 24.74498274699181
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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