Corrigendum to “Advancing deep learning-based acoustic leak detection methods towards application for water distribution systems from a data-centric perspective” [Water Research 261(2024) 121999]

Yipeng Wu, Xingke Ma, Guancheng Guo, Tianlong Jia, Yujun Huang, Shuming Liu*, Jingjing Fan, Xue Wu

*Corresponding author for this work

Research output: Contribution to journalComment/Letter to the editorScientificpeer-review

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Abstract

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.

Original languageEnglish
Article number122080
Number of pages1
JournalWater Research
Volume262
DOIs
Publication statusPublished - 2024

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