Shu Fang1, Lei Huang1, Yi Wan2, Weize Sun1, *, Jingxin Xu3
CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 541-555, 2020, DOI:10.32604/cmc.2020.010066
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… More >
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