Support Vector Machine Based Data Cleaning for Water Supply Network Monitoring

Water supply network monitoring data usually contains a large number of anomalous data, which is difficult to reflect the real working condition of the water supply pipe, affecting the accuracy of the wind power prediction, and thus causing certain economic losses. In order to solve this problem, we analyze the characteristics of the abnormal data and propose a data cleaning method for water supply pipe network based on support vector machine, and use the seasonal differential auto regressive moving average model as a comparison. The seasonal differential auto regressive moving average model is used as a comparison to determine the advantages of its high accuracy rate of anomaly identification, which can better repair the errors and missing data in the pipe network monitoring data and provide a more reliable basis for analyzing and making decisions. In this paper, the monitoring data of water supply pipe network in a southern city were collected and preprocessed, including the filling of missing values and the detection of data outliers. Next, we modeled and predicted the data using support vector machines to identify potential outliers. Then, for potential outliers, we compare them with the original data for secondary determination. Finally, we use support vector machine for data filtering and use the cleaned data for the operation and management of the pipe network system. The effectiveness of the method in improving the quality and accuracy of the monitoring data was demonstrated by testing and analyzing the actual data. Future research could further explore and optimize the method to improve the operational efficiency and reliability of the pipe network system.