eprintid: 1322
rev_number: 7
eprint_status: archive
userid: 7
dir: disk0/00/00/13/22
datestamp: 2025-01-09 21:19:35
lastmod: 2025-01-09 21:19:35
status_changed: 2025-01-09 21:19:35
type: article
metadata_visibility: show
creators_name: Tshireletso, Thalosang
creators_name: Moyo, Pilate
title: Integrating Prophet Forecasting with Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) for Early Warning System in Dam Deformation Monitoring
ispublished: pub
subjects: TA
divisions: j6
full_text_status: public
keywords: Gaussian Mixture Model, Hidden Markov Model, Prophet Model, Dam Deformation Forecasting
abstract: Ensuring dam safety requires a monitoring system that can predict deformations and detect anomalies in real-time. This study combines the forecasting capabilities of the Prophet model with the real-time anomaly detection of a Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) framework. The Prophet model analyses historical deformation data to forecast future deformations, enabling early issue identification. The GMM-HMM framework continuously monitors incoming data to detect deviations from predictions. Results shows that the GMM-HMM, with 10 components and a Mahalanobis distance threshold of 0.1, achieved a precision of 0.602, recall of 1.0, and F-1 score of 0.751, ensuring high sensitivity and accurate anomaly detection on. The GMM-HMM was then used to detect anomalies on Prophet forecasted radial deformations. Anomalies were detected on upper limit and lower limit deformations. This combined approach enhances dam safety by integrating predictive and real-time monitoring capabilities, offering a comprehensive early warning system for dam infrastructure.
date: 2024-09-15
publication: Journal of Civil Engineering and Urbanism
volume: 14
number: 3s
publisher: Scienceline Publication
pagerange: 212-219
id_number: 10.54203/jceu.2024.22
refereed: TRUE
issn: 2252-0430
official_url: http://dx.doi.org/10.54203/jceu.2024.22
j_index: notindex
citation:    (2024)     Integrating Prophet Forecasting with Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) for Early Warning System in Dam Deformation Monitoring.   Journal of Civil Engineering and Urbanism.  pp. 212-219.  ISSN 2252-0430   
document_url: http://eprints.science-line.com/id/eprint/1322/1/JCEU14%283s%29212-219%2C2024.pdf