Water Quality Data Imputation via A Fast Latent Factorization of Tensors with PID-based Optimizer
This work addresses data imputation for water quality analysis, which is crucial for environmental governance, but it appears incremental as it builds on existing latent factorization methods with a PID-based optimizer.
The paper tackles the problem of missing values in water quality data by proposing a Fast Latent Factorization of Tensors (FLFT) model, which improves convergence rate and achieves higher accuracy compared to state-of-the-art models in real-world datasets.
Water quality data can supply a substantial decision support for water resources utilization and pollution prevention. However, there are numerous missing values in water quality data due to inescapable factors like sensor failure, thereby leading to biased result for hydrological analysis and failing to support environmental governance decision accurately. A Latent Factorization of Tensors (LFT) with Stochastic Gradient Descent (SGD) proves to be an efficient imputation method. However, a standard SGD-based LFT model commonly surfers from the slow convergence that impairs its efficiency. To tackle this issue, this paper proposes a Fast Latent Factorization of Tensors (FLFT) model. It constructs an adjusted instance error into SGD via leveraging a nonlinear PID controller to incorporates the past, current and future information of prediction error for improving convergence rate. Comparing with state-of-art models in real world datasets, the results of experiment indicate that the FLFT model achieves a better convergence rate and higher accuracy.