What does this research mean for the field?

Although machine unlearning has not yet been directly applied to water distribution systems, cross-domain synthesis indicates it can be theoretically adapted to remove faulty, manipulated, or sensitive data while preserving safety-critical operational evidence. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

To evaluate whether and how machine unlearning methods can be transferred to artificial intelligence models used in water distribution systems to eliminate corrupted or obsolete data.

August 22, 2026Journal of Infrastructure Preservation and ResilienceOpen Access

Machine unlearning for water distribution systems: a scoping review and verification-guided transferability framework

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Authors

AMAli Maleki MAMohammadreza Alizadeh Tataki Afshar ANAmin Nejat

Discussion

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Overview

Scoping review identifies a total gap in machine unlearning applications for water distribution systems, suggesting framework needs for safely removing faulty or poisoned operational data.

Key Points

To evaluate whether and how machine unlearning methods can be transferred to artificial intelligence models used in water distribution systems to eliminate corrupted or obsolete data.
Conducted a scoping review across Scopus, Web of Science, and Google Scholar on December 10, 2024, evaluating intersections between machine unlearning and water networks.
Screened and synthesized 51 core machine unlearning studies and 831 water distribution machine learning records.
Mapped exact, approximate, partition-based, federated, and certified unlearning paradigms against water system data dependencies, operational risks, and physical constraints.
Found zero direct, validated applications of machine unlearning deployed within water distribution systems, revealing two structurally disconnected fields.
Categorized unlearning targets into legitimate candidates (faulty, poisoned, obsolete, or privacy-sensitive data) versus safety-critical evidence (verified leaks, pressure transients, contamination) that must be preserved.
Formulated an evidence-informed transferability framework prioritizing verification of physical consistency, false-negative risk, predictive utility, and operational safety over baseline full retraining.

Cite This Study

Maleki et al. (2026) studied this question.

synapsesocial.com/papers/6a895e96ca7ade938187cb5f https://doi.org/10.1186/s43065-026-00210-1

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August 22, 2026Journal of Infrastructure Preservation and ResilienceOpen Access

Machine unlearning for water distribution systems: a scoping review and verification-guided transferability framework

View Full Paper

Ask AI

Bookmark

Authors

AMAli Maleki MAMohammadreza Alizadeh Tataki Afshar ANAmin Nejat

Discussion

Member takes

Overview

Scoping review identifies a total gap in machine unlearning applications for water distribution systems, suggesting framework needs for safely removing faulty or poisoned operational data.

Key Points

To evaluate whether and how machine unlearning methods can be transferred to artificial intelligence models used in water distribution systems to eliminate corrupted or obsolete data.
Conducted a scoping review across Scopus, Web of Science, and Google Scholar on December 10, 2024, evaluating intersections between machine unlearning and water networks.
Screened and synthesized 51 core machine unlearning studies and 831 water distribution machine learning records.
Mapped exact, approximate, partition-based, federated, and certified unlearning paradigms against water system data dependencies, operational risks, and physical constraints.
Found zero direct, validated applications of machine unlearning deployed within water distribution systems, revealing two structurally disconnected fields.
Categorized unlearning targets into legitimate candidates (faulty, poisoned, obsolete, or privacy-sensitive data) versus safety-critical evidence (verified leaks, pressure transients, contamination) that must be preserved.
Formulated an evidence-informed transferability framework prioritizing verification of physical consistency, false-negative risk, predictive utility, and operational safety over baseline full retraining.

Cite This Study

Maleki et al. (2026) studied this question.

synapsesocial.com/papers/6a895e96ca7ade938187cb5f https://doi.org/10.1186/s43065-026-00210-1

View Full Paper

Ask AI

Bookmark