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Water Supply

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Interpretable deep learning for acoustic leak detection in water distribution systems.

Water research
Leak detection is crucial for ensuring the safety of water systems and conserving water resources. However, current research on machine learning methods for leak detection focuses excessively on model development while neglecting model interpretabili...

Network embedding: The bridge between water distribution network hydraulics and machine learning.

Water research
Machine learning has been increasingly used to solve management problems of water distribution networks (WDNs). A critical research gap, however, remains in the effective incorporation of WDN hydraulic characteristics in machine learning. Here we pre...

A machine learning approach to estimate domestic use of public and private water sources in the United States.

Water research
In the United States, people obtain water for household use from one of two sources. Public water systems, which are subject to rules and regulations under the Safe Drinking Water Act, or private sources such as domestic wells, which are not subject ...

Predicting few disinfection byproducts in the water distribution systems using machine learning models.

Environmental science and pollution research international
Concerns regarding disinfection byproducts (DBPs) in drinking water persist, with measurements in water treatment plants (WTPs) being relatively easier than those in water distribution systems (WDSs) due to accessibility challenges, especially during...

The reanalysis of a new strategy for groundwater level prediction using combined simulation of machine learning and Muskingum methods under ecological water replenishment.

Environmental research
Due to its multi-functionality, ecological water replenishment (EWR) has been an important measure for restoring aquifers. However, suitable prediction methods need to be selected for the unique fluctuation exhibited by groundwater level (GWL) in the...

Water level estimation in sewage pipes using texture-based methods and machine learning algorithms.

Water science and technology : a journal of the International Association on Water Pollution Research
Water companies use closed-circuit television (CCTV) to inspect the condition of sewage pipes. The reports generated by surveyors help companies to plan for the maintenance and rehabilitation of sewage pipes. A surveyor needs to record the water leve...

Spectral physics-informed neural network for transient pipe flow simulation.

Water research
Accurate wave propagation models are essential for effective monitoring and automated localization in water supply pipelines. The recently-established Physics-Informed Neural Networks (PINNs) can enhance the wave analysis and reduce uncertainties by ...

Explainable deep learning models for predicting water pipe failures.

Journal of environmental management
Failures within water distribution networks (WDNs) lead to significant environmental and economic impacts. While existing research has established various predictive models for pipe failures, there remains a lack of studies focusing on the probabilit...

Enhancing drinking water safety: Real-time prediction of trihalomethanes in a water distribution system using machine learning and multisensory technology.

Ecotoxicology and environmental safety
Prolonged exposure to high concentrations of trihalomethanes (THMs) may generate human health risks due to their carcinogenic and mutagenic properties. Therefore, monitoring THMs in drinking water distribution systems (DWDS) is essential. This study ...

Predicting determinants of unimproved water supply in Ethiopia using machine learning analysis of EDHS-2019 data.

Scientific reports
Over 2 billion people worldwide are impacted by the global dilemma of access to clean and safe drinking water. The problem is most acute in low-income nations, where many people still use unimproved water sources such as exposed wells and surface wat...