# A Causal Convolutional Low-rank Representation Model for Imputation of Water Quality Data

arXiv: https://arxiv.org/abs/2504.15209 · HTML: https://mlsanity.com/paper/2504.15209/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 82 references that are in the map.
- First posted on arXiv 21 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Missing Value Estimation using Clustering and Deep Learning within Multiple Imputation Framework](https://mlsanity.com/paper/2202.13734/) (2022)

## Built on by (most cited first, 2 of 2)

- [Neural Factorization-based Bearing Fault Diagnosis](https://mlsanity.com/paper/2512.06837/) (2025)
- [A Hybrid Tucker-LSTM Tensor Network Model for SOC Prediction in Electric Vehicles](https://mlsanity.com/paper/2605.13200/) (2026)

## Abstract (arXiv)

The monitoring of water quality is a crucial part of environmental protection, and a large number of monitors are widely deployed to monitor water quality. Due to unavoidable factors such as data acquisition breakdowns, sensors and communication failures, water quality monitoring data suffers from missing values over time, resulting in High-Dimensional and Sparse (HDS) Water Quality Data (WQD). The simple and rough filling of the missing values leads to inaccurate results and affects the implementation of relevant measures. Therefore, this paper proposes a Causal convolutional Low-rank Representation (CLR) model for imputing missing WQD to improve the completeness of the WQD, which employs a two-fold idea: a) applying causal convolutional operation to consider the temporal dependence of the low-rank representation, thus incorporating temporal information to improve the imputation accuracy; and b) implementing a hyperparameters adaptation scheme to automatically adjust the best hyperparameters during model training, thereby reducing the tedious manual adjustment of hyper-parameters. Experimental studies on three real-world water quality datasets demonstrate that the proposed CLR model is superior to some of the existing state-of-the-art imputation models in terms of imputation accuracy and time cost, as well as indicating that the proposed model provides more reliable decision support for environmental monitoring.