Outlier Detection for Water Supply Data Based on Joint Auto-Encoder

作     者：Shu Fang Lei Huang Yi Wan Weize Sun Jingxin Xu 

作者机构：Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics(SZ)College of Electronics and Information EngineeringShenzhen UniversityShenzhen518061China Water Resources Management Center of Ministry of Water ResourcesBeijingChina Departmet of Housing and Public WorksQueenslandAustralia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2020年第64卷第7期

页      面：541-555页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The work described in this paper was supported by the National Natural Science Foundation of China(NSFC)under Grant No.U1501253 and Grant No.U1713217. 

主　　题：Water supply data outlier detection auto-encoder deep learning 

摘      要：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,and,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.