A Nonlinear Low-rank Representation Model with Convolutional Neural Network for Imputing Water Quality Data
This addresses data missing issues in water quality analysis for ecological environmental protection, representing an incremental improvement.
The paper tackles the problem of missing water quality monitoring data by proposing a Neural Tucker Convolutional Network (NTCN) model for imputation, which outperforms state-of-the-art methods on three real-world datasets in terms of accuracy.
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.