Research on Intelligent Water Management Detection Based on the K-means Clustering Algorithm

In the context of big data, intelligent water management detection has become an essential component of modern urban construction. Since water supply systems may experience system failures that lead to water leakage, it is necessary to establish a flow anomaly detection model for leak detection. This paper proposes an intelligent water management detection model based on the K-means clustering algorithm, facilitating unified flow anomaly detection under different water flow data collection patterns to promptly identify water management issues. Utilizing water flow data from eight virtual regions, the data is first preprocessed using 3σ anomaly detection and moving average filtering. Subsequently, the data undergoes debiasing and max-min normalization to standardize data from different patterns. The K-means clustering analysis is applied to train the classification criteria for normal and abnormal water flow categories and to classify them, with the abnormal water flow category representing the anomalies that need to be detected. This model effectively conducts intelligent water management detection.