Open Access
ARTICLE
Outlier Detection for Water Supply Data Based on Joint Auto-Encoder
Shu Fang1, Lei Huang1, Yi Wan2, Weize Sun1, *, Jingxin Xu3
1 Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics (SZ), College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518061, China.
2 Water Resources Management Center of Ministry of Water Resources, Beijing, China.
3 Departmet of Housing and Public Works, Queensland, Australia.
* Corresponding Author: Weize Sun. Email: .
Computers, Materials & Continua 2020, 64(1), 541-555. https://doi.org/10.32604/cmc.2020.010066
Received 10 February 2020; Accepted 08 March 2020; Issue published 20 May 2020
Abstract
With the development of science and technology, the status of the water
environment has received more and more attention. In this paper, we propose a deep
learning model, named a Joint Auto-Encoder network, to solve the problem of outlier
detection in water supply data. The Joint Auto-Encoder network first expands the size of
training data and extracts the useful features from the input data, and then reconstructs
the input data effectively into an output. The outliers are detected based on the network’s
reconstruction errors, with a larger reconstruction error indicating a higher rate to be an
outlier. For water supply data, there are mainly two types of outliers: outliers with large
values and those with values closed to zero. We set two separate thresholds, τ
1 and τ
2,
for the reconstruction errors to detect the two types of outliers respectively. The data
samples with reconstruction errors exceeding the thresholds are voted to be outliers. The
two thresholds can be calculated by the classification confusion matrix and the receiver
operating characteristic (ROC) curve. We have also performed comparisons between the
Joint Auto-Encoder and the vanilla Auto-Encoder in this paper on both the synthesis data
set and the MNIST data set. As a result, our model has proved to outperform the vanilla
Auto-Encoder and some other outlier detection approaches with the recall rate of 98.94
percent in water supply data.
Keywords
Cite This Article
S. Fang, L. Huang, Y. Wan, W. Sun and J. Xu, "Outlier detection for water supply data based on joint auto-encoder,"
Computers, Materials & Continua, vol. 64, no.1, pp. 541–555, 2020. https://doi.org/10.32604/cmc.2020.010066
Citations