Neural Tucker Convolutional Network for Water Quality Analysis

TL;DR

This paper introduces a Neural Tucker Convolutional Network that effectively imputes missing water quality data by capturing complex interactions and spatiotemporal features, significantly improving accuracy over existing methods.

Contribution

The paper presents a novel NTCN model that combines Tucker tensor interactions with 3D convolution for enhanced water quality data imputation.

Findings

Outperforms state-of-the-art imputation models in accuracy

Effectively captures complex mode-wise feature interactions

Extracts fine-grained spatiotemporal features

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.