# A Multi-Leak Identification Scheme Using Multi-Classification for Water Distribution Infrastructure
## 4 Conclusions  
Aged water distribution infrastructure (WDI) is a global issue for water wastage due to leakage. It is necessary to develop automated leak detection to avoid economic loss and save freshwater resources. In this paper, a new multi-classification-based multi-leak identification (MC-MLI) scheme was proposed. After the data analysis, ten (10) features were extracted to develop classifiers. A new adaptive kernel was developed to transform the input features into high dimensional feature space. The features were weighted in the adaptive kernel with respect to their importance levels to improve identification performances. Moreover, a multi-classification with a parallel structure was designed to integrate multiple binary classifiers. Each pipe section was monitored by a unique binary classifier. Owing to the parallel structure of multi-classification, single/multiple detection(s) can be performed without activating all classifiers. Additionally, it facilitates the integration of a  
Figure 6: Performance evaluation of MC-MLI under different leak rates.  
newly added pipe section. An extra classifier is trained for the newly added pipe section without reconfiguration of the whole identification system. Multiple classifier scenarios were designed to adapt to various performance requirements (e.g., accuracy, detection time, etc.). All scenarios had different performances in terms of accuracies, sensitivities, specificities and detection times. The performance requirements can be varied according to the pipes' importance levels. Therefore, the proposed MC-MLI has high detection efficiency, high system flexibility, and high system adaptiveness. An experiment involving multiple leak scenarios was performed to evaluate the proposed MC-MLI. The results demonstrate that MC-MLI achieves 96% accuracy, 96% sensitivity, and 95% specificity. The average detection time was about 5 s. The improvement in identification performance was in the range of 15% to 30% compared to prime classification schemes.  
In the experiment, the same types of flow sensors and same types of pressure sensors were adopted. They had the same connection interface (i.e., serial port) and showed similar measurement precision. In reality, different types of sensors from different manufacturers may be utilized, which may pose difficulty when replacing sensors in the detection system. Considering this situation, the interoperability of different sensors based on the IEEE P2668 standard will be studied in future work to make sensors \"plug-and-play\". Besides that, the accuracy of sensor data needs to be guaranteed. By evaluating the accuracy of sensor data based on the IEEE P2668 standard, the reliability of the detection system will be further improved.  
Conceptualization, writing--original draft preparation, data collection, software, Y.W.; supervision and project administration, K.F.T.; methodology and formal analysis, C.K.W.; literature collection, H.W.; validation and investigation, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Electrical and Mechanical Services Department of Hong Kong, grant number 9211292.  
Not applicable.  
The authors declare no conflict of interest.