Detection algorithm of abnormal characteristics of urban domestic water quality based on K-means clustering

Abstract

In order to solve the problem of low detection accuracy in water quality anomaly detection, an urban domestic water quality anomaly detection algorithm based on K-means clustering is proposed. Firstly, by constructing the water quality feature extraction system and calculating the pollutant content by fluorescence method, the water quality feature extraction and pollutant content determination are completed. Then, normalise the data and introduce root mean square error to remove redundancy and complete the preprocessing. Finally, taking the pH value, ammonia nitrogen, oxygen consumption, chromaticity and turbidity of urban domestic water quality as abnormal values, the trust degree and data cluster distance between data are calculated through K-means clustering, and the abnormal characteristic detection model of urban domestic water quality is constructed to complete the detection. The results show that the proposed method has high accuracy in detecting the abnormal characteristics of urban domestic water quality.