Designing AI-Powered Early Warning Systems to Detect and Prevent Critical Failures in Aging Infrastructure Across Urban Landscapes

AI-powered early warning systems offer a proactive approach to detecting and preventing critical failures in aging urban infrastructure. This study explores the design and implementation of machine learning models for real-time monitoring and failure prediction. Results demonstrate significant improvements in predictive accuracy and response times, supporting the sustainability and resilience of urban landscapes.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-03
DOI
https://doi.org/10.5281/zenodo.14268706
Primary Topic
Infant Nutrition and Health
Type
article
Field-Weighted Citation Impact
0.00
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article

Designing AI-Powered Early Warning Systems to Detect and Prevent Critical Failures in Aging Infrastructure Across Urban Landscapes

Zenodo (CERN European Organization for Nuclear Research)
Infant Nutrition and Health
article

Designing AI-Powered Early Warning Systems to Detect and Prevent Critical Failures in Aging Infrastructure Across Urban Landscapes

article en

Abstract

AI-powered early warning systems offer a proactive approach to detecting and preventing critical failures in aging urban infrastructure. This study explores the design and implementation of machine learning models for real-time monitoring and failure prediction. Results demonstrate significant improvements in predictive accuracy and response times, supporting the sustainability and resilience of urban landscapes.

Zenodo (CERN European Organization for Nuclear Research)
King Abdullah University of Science and Technology (SA)
Openalex Percentile: Top 58%
Infant Nutrition and Health
DOIOpen Access PDFOpenAlex
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Designing AI-Powered Early Warning Systems to Detect and Prevent Critical Failures in Aging Infrastructure Across Urban Landscapes · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS