用张量卷积模型填补水质数据缺失，提升分析准确性。
Neural Tucker Convolutional Network for Water Quality Analysis
- 构建张量交互表示，捕捉多维度特征关联
- 3D卷积从张量中提取精细时空特征，提升补全精度
- 适合处理长期监测中的数据缺失问题
水质监测是生态保护的核心。由于传感器故障等不可控因素，长期监测中常出现数据缺失，给水质分析带来挑战。本文提出神经张量卷积网络（NTCN）用于水质数据补全，核心包括：将不同模式实体编码为嵌入向量，通过外积构造张量以捕捉复杂的模式间特征交互；利用3D卷积从交互张量中提取细粒度时空特征。在三个真实水质数据集上的实验表明，所提NTCN模型在准确率上优于多个前沿补全模型。
原文摘要 · Abstract (English)
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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