Robust night flow analysis in water distribution networks: A BiLSTM deep autoencoder approach

Abstract

Night flow analysis is predominantly used to identify incipient (gradual) leakages in real-life Water Distribution Networks (WDNs). However, due to extreme stochastic demand especially in residentially dominated district metering areas (DMAs) and the emergence of sleepless cities, traditional night flow analysis methods have become inefficient. This study proposes a semi-supervised sequence-to-sequence Bidirectional Long Short-Term Memory (BiLSTM) deep Auto Encoder (AE) method for night flow analysis in WDNs. To enhance the generalisation power of the deep AE, a vigorous data augmentation procedure based engineering domain knowledge and inherent properties of DMA is presented. The proposed method learns the underlying benign patterns in night flow (2:00 am to 4:00 am) and identifies significant deviations that represent the development of incipient leakages. The method was validated on residential, commercial, and industrial DMAs in a real-life WDN in the Ålesund Municipality of Norway. The results from the study showed that the proposed method is a robust and superior alternative to traditional night flow under extreme stochastic consumer demand. The proposed BiLSTM deep AE method was able to identify unreported leakages in the DMAs within 1 day or at most 4 days compared with at least 15 days identification time by traditional minimum night flow (MNF) and average night flow (ANF) analyses. Additionally, traditional minimum night flow analysis also produced more false positives compared with the proposed method. The results also revealed the usage of point estimates to represent night flow as done in MNF and ANF analyses fail to capture all salient information regarding night flow compared to the BiLSTM deep AE.