Cited by Application of machine learning techniques for ambulance coverage prediction

Application of machine learning to leakage detection of fluid pipelines in recent years: A review and prospect DOI

Jianwu Chen,

Xiao Wu, Zhibo Jiang

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 116857 - 116857

Published: Jan. 1, 2025

Language: Английский

Citations

Ageing Underground Water Pipelines: Time-to-Failure Models, Gaps and Future Directions DOI

Beenish Bakhtawar, Tarek Zayed, Ibrahim Abdelfadeel Shaban

et al.

Water Research X, Journal Year: 2025, Volume and Issue: unknown, P. 100331 - 100331

Published: March 1, 2025

Language: Английский

Citations

Safeguarding Pipeline Integrity Through Stacked Ensemble Learning and Data Fusion DOI

Hussein Alaa Hussein,

Sharafiz B. Abdul Rahim,

Faizal Mustapha

et al.

International journal of mechanical system dynamics, Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Abstract This research presents a novel approach to pipeline Structure Health Monitoring (SHM) by utilizing frequency response function signals and integrating advanced data‐driven techniques detect evaluate vibration responses regarding loose bolts, scale deposits within pipelines, cracks at supports, aiming determine the effectiveness of artificial neural networks (ANN) an ensemble learning in detecting aforementioned damages through approach. The starts recording 6500 samples captured two accelerometers, related 11 replicated structural scenarios. demonstrated potential principal component analysis (PCA) dimensionality reduction, achieving approximately 81% reduction data set 1 acquired accelerometer around 79.5% 2 2, without significant loss information. Additionally, ANN base models were employed for fault recognition classification, over 99.88% accuracy mean squared error values ranging from 0.00006 0.00019. A innovation this work lies implementation approach, which integrates strengths models, showcasing outstanding performance that was proved consistent across multiple iterations, effectively mitigating weaknesses providing reliable classification prediction system. underscores combining PCA, ANN, k‐fold cross‐validation, SHM improved reliability safety. findings highlight broader applications methodology real‐world scenarios, addressing urgent challenges faced infrastructure owners operators.

Language: Английский

Citations

Sinkhole Risk-Based Sensor Placement for Leakage Localization in Water Distribution Networks with a Data-Driven Approach DOI

Gabriele Medio, Giada Varra, Çağrı Alperen İnan

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(12), P. 5246 - 5246

Published: June 20, 2024

Leakages from damaged or deteriorated buried pipes in urban water distribution networks may cause significant socio-economic and environmental impacts, such as depletion of resources sinkhole events. Sinkholes are often caused by internal erosion fluidization the soil surrounding leaking pipes, with formation cavities that eventually collapse. This turn causes road disruption building foundation damage, possible victims. While loss precious is a well-known problem, less attention has been paid to anthropogenic events generated leakages systems. With view improving smart resilience sustainability areas, this study introduces an innovative framework localize based on Machine learning model (for training evaluation candidate sets pressure sensors) Genetic algorithm optimal sensor set positioning) goal detecting mitigating potential hydrogeological due leakage most sensitive/critical locations. The application methodology synthetic case literature real-world scenario shows also contributes reducing resources.

Language: Английский

Citations

Application of machine learning techniques for ambulance coverage prediction DOI

Raja Manaa,

Maroua Ben Talha,

Lina Aboueljinane

et al.

Published: May 2, 2024

Language: Английский

Citations