The authors regret the implementation order of data augmentation and data splitting was incorrectly stated. Data augmentation should be implemented after data splitting. While the correct implementation order and its impacts on leakage detection performance were accurately discussed in Section 3.2 “Biased results caused by data leakage”, there were errors in the highlights, abstract, and conclusions sections. The corrections are as follows: 1. The second highlight should be corrected to “Data augmentation after splitting prevents biased results due to data leakage.”2. In the abstract, the corresponding sentence should be corrected to “Results indicate the importance of implementing data augmentation after data splitting to prevent data leakage and overly optimistic outcomes.”3. In the second paragraph of the conclusions, the first sentence should be corrected to “It is recommended to implement data augmentation after data splitting to avoid data leakage, which could lead to biased and overly optimistic results.”The authors would like to apologise for any inconvenience caused.

Leakage in water distribution systems is a significant problem worldwide, leading to wastage of water resources, compromised water quality and excess energy consumption. Leakage detection is essential to reduce the duration of leaks and data-driven methods are increasingly being used for this purpose. However, these models are data hungry and available observed data, especially leakage data, is limited in most cases. In addition, these data need to be manually processed to label whether leaks occur, which is time-consuming and costly. These are significant obstacles for the development and application of these methods. This article provides a comprehensive review of relevant journal papers, categorizing all data-driven methods into unsupervised anomaly detection, semi-supervised anomaly detection and supervised classification methods based on how the data are utilized for developing these methods. In addition, strategies to address data limitations are summarized from both data and model perspectives, including data creation, reduction of a model's data requirements and knowledge transfer. After detailing these strategies, research gaps are identified. Based on these, future research directions are suggested, highlighting the need for further research in data augmentation, development of semi-supervised classification methods, exploration of multi-classification methods with model updating mechanisms, and development of novel knowledge transfer methods.