An iterative method for leakage zone identification in water distribution networks based on machine learning

For leakage identification in water distribution networks, if each node is used as a category label of the classifier model, the accuracy of the classifier model will be low because of similar leakage characteristics. By clustering the nodes with similar leakage characteristics and using all the possible combinations of leakages as the category labels of the classifier model, the accuracy of the classifier model for leakage location can be improved. An iterative method combiningk-means clustering with the random forest classifier is proposed to identify the leakage zones. In each iteration,k-means clustering is used to divide the leakage zone identified in the previous iterations into two zones, and then, the random forest classifier is used to identify the leakage zones and the number of leakages in each leakage zone. As the number of iterations increases, the number of candidate leakage zones and sensors that conduct leakage zone identification decreases. Thus, feature selection can be used in each iteration to select the minimum number of sensors for model training without affecting identification accuracy. Three leakage scenarios are considered: a single leakage, two simultaneous leakages, and four simultaneous leakages. A benchmark case is presented in this study to demonstrate the effectiveness of the proposed method. The influences of the number of pressure sensors and Gaussian noise level on the identification results are also discussed. Results indicate that the proposed method is effective for identifying simultaneous leakages.

Automated summary

An iterative method combining k-means clustering with the random forest classifier is proposed for leakage identification in water distribution networks to improve the accuracy of the classifier model. The method effectively identifies simultaneous leakages in different scenarios, demonstrating its effectiveness in leak detection.