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Water scarcity as well as social and economic damages caused by the increasing amounts of non-revenue water in the water distribution networks (WDNs) have been prompting innovative solutions. A great deal of potable water is wasted due to leakage in the WDNs all over the world. Hence, various leak detection approaches have been explored, including the promising application of acoustic devices. Exploiting the benefits of technological advances in acoustic devices, signal processing, and machine learning (ML), this study aimed to develop a sophisticated system for leak detection in WDNs. Different from laboratory-based studies, this study was conducted on real WDNs in Hong Kong and lasted for about two years. Utilizing acoustic emissions acquired using wireless noise loggers, various ML algorithms were explored to develop inspection models for in-service and buried WDNs. ML classification algorithms can identify patterns in the acquired signals for leak and no-leak statuses. Thus, a combination of features describing acoustic signals in time and frequency domains was utilized to facilitate the development of ML models. Separately for metal and non-metal WDNs, ten well-known ML algorithms were used to develop leak detection models. The validation results demonstrate the promising application of noise loggers and ML for leak detection in real WDNs. Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Learning (DL) leak detection models demonstrated a largely stable performance and a very good accuracy, particularly for new unlabelled cases. [ABSTRACT FROM AUTHOR]

Diversity of particle-attached and free-living marine bacteria in Victoria Harbor, Hong Kong, and its adjacent coastal and estuarial environments was investigated using DNA fingerprinting and clone library analysis. Denaturing gradient gel electrophoresis (DGGE) analysis of 16S rRNA genes showed that bacterial communities in three stations of Victoria Harbor were similar, but differed from those in adjacent coastal and estuarine stations. Particle-attached and free-living bacterial community composition differed in the Victoria Harbor area. DNA sequencing of 28 bands from DGGE gel showed Alphaproteobacteria was the most abundant group, followed by the Bacteroidetes, and other Proteobacteria. Bacterial species richness (number of DGGE bands) differed among stations and populations (particle-attached and free-living; bottom and surface). BIOENV analysis indicated that the concentrations of suspended solids were the major contributing parameter for the spatial variation of total bacterial community structure. Samples from representative stations were selected for clone library (548 clones) construction and their phylogenetic distributions were similar to those of sequences from DGGE. Approximately 80% of clones were affiliated to Proteobacteria, Bacteroidetes and Cyanobacteria. The possible influences of dynamic pollution and hydrological conditions in the Victoria Harbor area on the particle-attached and free-living bacterial community structures were discussed. [ABSTRACT FROM AUTHOR]