Low-cost early warning for direct drinking water pipeline leakage based on SMOTE-ENN-GWO-SVM model

Abstract

[Objective] Leakage in direct drinking water pipelines has long been a persistent challenge in China's water supply industry. It not only affects residents' normal water usage but also leads to substantial drinking water waste and brings significant economic losses to water supply enterprises. Current leakage detection technologies face challenges such as high equipment costs, insufficient intelligence levels, and imbalanced data categories. [Methods] To address these issues, a low-cost pipeline leakage early warning model based on the principle of constant pressure water supply was proposed. Building upon the principle of constant pressure water supply, the dynamic changes in operational parameters of water supply frequency converter, such as frequency, voltage, current, and rotational speed, were used to establish the SMOTE-ENN-GWO-SVM leakage early warning model by integrating intelligent algorithms and machine learning technologies. The SMOTE algorithm was used to perform oversampling on the sample data to balance the class distribution, the Edited Nearest Neighbors(ENN) algorithm was applied to clean noisy samples, and the grey wolf optimizer was used to adjust the key hyperparameters of the support vector machine(SVM) to enhance model performance. [Results] The result showed that the SMOTE-ENN-GWO-SVM model achieved an accuracy of 98.16% and an F1 score of 0.952 3, both outperforming the comparative models. [Conclusion] This method significantly improves the accuracy and robustness of leakage detection. Its characteristics of low cost and high precision meet the practical application requirements for responsive and reliable leakage detection in direct drinking water systems, providing technical support for improving the efficiency of urban water resource management.

Keywords

direct drinking water pipeline / support vector machine / grey wolf optimizer / imbalanced dataset / constant pressure water supply / water supply frequency converter / leakage warning / influencing factors