A Nonlinear Low-rank Representation Model with Convolutional Neural Network for Imputing Water Quality Data

TL;DR

This paper introduces a nonlinear low-rank representation model combined with CNNs to improve the accuracy of imputing missing water quality data by capturing complex temporal and local features.

Contribution

It proposes a novel NLR-CNN model that effectively fuses temporal and multidimensional features for water quality data imputation, outperforming existing methods.

Findings

Significantly better imputation accuracy than state-of-the-art models

Effective in handling high-dimensional and sparse water quality data

Demonstrated robustness across three real datasets

Abstract

The integrity of Water Quality Data (WQD) is critical in environmental monitoring for scientific decision-making and ecological protection. However, water quality monitoring systems are often challenged by large amounts of missing data due to unavoidable problems such as sensor failures and communication delays, which further lead to water quality data becoming High-Dimensional and Sparse (HDS). Traditional data imputation methods are difficult to depict the potential dynamics and fail to capture the deep data features, resulting in unsatisfactory imputation performance. To effectively address the above issues, this paper proposes a Nonlinear Low-rank Representation model (NLR) with Convolutional Neural Networks (CNN) for imputing missing WQD, which utilizes CNNs to implement two ideas: a) fusing temporal features to model the temporal dependence of data between time slots, and b) Extracting nonlinear interactions and local patterns to mine higher-order relationships features and achieve deep fusion of multidimensional information. Experimental studies on three real water quality datasets demonstrate that the proposed model significantly outperforms existing state-of-the-art data imputation models in terms of estimation accuracy. It provides an effective approach for handling water quality monitoring data in complex dynamic environments.