Improving Urban Water Security with Modern Analytics: Advancing Water Systems Performance and Longevity
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".
Deadline for manuscript submissions: 20 February 2027 | Viewed by 4
Special Issue Editors
Interests: machine learning; asset management; infrastructure; risk assessment; flood forecasting; low tech water treatment; climate change; data mining; emerging contaminants; water treatment
Special Issues, Collections and Topics in MDPI journals
Interests: water and wastewater treatment; persistent and emerging contaminants; AI aided environmental modeling; decision making; risk assessment; marine and coastal protection; climate change
Special Issues, Collections and Topics in MDPI journals
Interests: water resources; reliability, uncertainty and systems analysis; designing, planning, operating and maintaining water assets
Special Issue Information
Dear Colleagues,
Cities are rapidly growing in spatial magnitude and density, and all need water to be delivered. This is resulting in issues such as pressure changes and increasing velocities, with many water utilities facing crises due to aging infrastructure. Aging pipes are deteriorating, pipe breaks are occurring, and preserving safe water for consumption is becoming increasingly difficult.
In response, many pipe break prediction models have been developed to identify which pipes are most likely to break next, assisting utilities providers in prioritizing pipe replacement, and many new procedures are being developed. The technologies embodied in different pipebreak models are widely variable.
With the combination of population growth and climate change, as well as issues related to pipe type, depth of burial, etc., there is an increased need for appropriate planning to improve on the age-based approach currently used to protect the integrity of the water delivered to individuals
Prof. Dr. Ed McBean
Prof. Dr. Bing Chen
Prof. Dr. Barbara J. Lence
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- pipe rehabilitation
- survival analysis
- infrastructure
- climate change
- low impact development
- management tool
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