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.

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