Application of secondary water supply water quality evaluation method based on K-means clustering and entropy method

Abstract

The secondary water supply water quality indicators monitored by urban water supply companies are usually residual chlorine, turbidity, pH, etc., and data can be continuously uploaded through online meters and used for analysis and early warning. However, there are two considerations in this process: one is that the process of data collection and transmission has a great impact on the quality of data samples; the other is that common water quality analysis methods are not suitable for secondary water quality analysis with low information density and large capacity. Based on the above considerations, this study carried out data quality evaluation on two main types of secondary water supply online monitoring indicators (residual chlorine, turbidity), and summarized 5 common data errors, 3 error-causing factors and 2 types of data error characteristics in order to support for the subsequent smart management of secondary water supply. A method for online monitoring and evaluation of secondary water supply water quality based on K-means clustering method and entropy method is proposed, and four main secondary water supply water quality influencing factors are analyzed for variance analysis and covariance analysis to provide secondary water supply operation and maintenance management. Learn from experience and suggestions.