Journal of Korea Water Resources Association ISSN:2799-8746(Print) 2799-8754(Online) 한국수자원학회 논문집

Bonafede, C.E., and Giudici, P. (2007). “Bayesian networks for enterprise risk assessment.” Physica A: Statistical Mechanics and Its Applications, Vol. 382, No. 1, pp. 22-28.

Chan, H., and Darwiche, A. (2003). “Reasoning about Bayesian network classifiers.” Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (UAI 2003), San Francisco, CA, U.S., pp. 107-115.

Chen, T.-T., and Wang, C.-H. (2017). “Fall risk assessment of bridge construction using Bayesian network transferring from fault tree analysis.” Journal of Civil Engineering and Management, Vol. 23, No. 2, pp. 273-282.

Deleu, T., Góis, A., Emezue, C., Rankawat, M., Lacoste-Julien, S., Bauer, S., and Bengio, Y. (2022). “Bayesian structure learning with generative flow networks.” Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022), Eindhoven, The Netherlands, pp. 1-15.

Encord (2023). F1 score in machine learning, accessed 4 July 2025, <https://encord.com/blog/f1-score-in-machine-learning/>.

Facility Integrated Information Management System (FMS) (2025). accessed 11 June 2025, <https://www.fms.or.kr>.

Futurense (2025). F1 score in machine learning: All you need to know in 2025, accessed 20 June 2025, <https://futurense.com/blog/f1-score-in-machine-learning>.

Green, D.K.E., and Jaspan, A. (2023). “Applied Bayesian structural health monitoring: Inclinometer data anomaly detection and forecasting.” Proceedings in Applied Mathematics and Mechanics (Conference proceedings from GAMM23), Vol. 23, No. 4, e202300132.

Harbecke, D., Chen, Y., Hennig, L., and Alt, C. (2022). “Why only Micro-F1? Class weighting of measures for relation classification.” Proceedings of the 1st Workshop on Efficient Benchmarking in NLP (NLP-Power 2022), pp. 32-41.

Infrastructure Management System (IMS) (2025). accessed 4 July 2025, <http://www.inframanage.go.kr>.

IREMS (2025). accessed 27 June 2025, <https://irems.kalis.or.kr>.

Ji, Z., Xia, Q., and Meng, G. (2015). “A review of parameter learning methods in Bayesian networks.” Advanced Intelligent Computing Theories and Applications: 11th International Conference on Intelligent Computing (ICIC 2015), Fuzhou, China, Vol. 9227. pp. 3-12.

Kamariotis, A., Chatzi, E., and Straub, D. (2022). “Value of information from vibration-based structural health monitoring extracted via Bayesian model updating.” Mechanical Systems and Signal Processing, Vol. 166, 108465.

Kim, S.-H., So, J.-M., Kang, S.-U., and Bae, D.-H. (2017). “Development and evaluation of dam inflow prediction method based on Bayesian method.” Journal of Korea Water Resources Association, Vol. 50, No. 7, pp. 489-502.

Kim, Y.-J., and Ahn, S.-M. (2017). “Effects of estuary barrage on biogenic silica (BSi) distribution in surface sediments of the Nakdong River estuary.” Journal of the Korean Society of Oceanography, Vol. 22, No. 4, pp. 172-186.

Korea Authority of Land & Infrastructure Safety (KALIS) (2021). Study on the establishment of detailed work system and standards for infrastructure management. pp. 1-8.

Korea Authority of Land & Infrastructure Safety (KALIS) (2025). 2024 national infrastructure safety statistical yearbook.

Korea Construction Standards Center (KCSC) (2025). accessed 15 July 2025, <https://www.kcsc.re.kr>.

Koski, T.J.T., and Noble, J. (2012). “A review of Bayesian networks and structure learning.” Mathematica Applicanda, Vol. 40, No. 1, pp. 51-103.

Li, Y., and Ji, W. (2023). “Bayesian-based dynamic forecasting of infrastructure restoration progress following extreme events.” International Journal of Disaster Risk Reduction, Vol. 85, 102451.

Ministry of Land, Infrastructure and Transport (MOLIT) (2017). Final report on the development of Korean performance grade evaluation technology for performance-based management of infrastructure. pp. 1-7.

Ministry of Land, Infrastructure and Transport (MOLIT) (2021). Enforcement rule of the special act on the safety and maintenance of infrastructure. National Law Information Center.

Moldstud Research Team (2025). How to evaluate NLP models - Key metrics, methods, and FAQs explained, accessed 2 April 2025, <https://moldstud.com/articles/p-how-to-evaluate-nlp-models-key-metrics-methods-and-faqs-explained?utm_source=chatgpt.com>.

Needham, C.J., Bradford, J.R., Bulpitt, A.J., and Westhead, D. (2007). “A primer on learning in Bayesian networks for computational biology.” PLoS Computational Biology, Vol. 3, No. 8, pp. 1409-1416.

Open Government Portal (2025). accessed 20 June 2025, <https://www.open.go.kr/com/main/mainView.do>.

Ou, X., Wu, Y., Qiu, W., Wu, B., and Jiang, J. (2022). “Dynamic Bayesian network for predicting tunnel-collapse risk in the case of incomplete data.” Journal of Performance of Constructed Facilities, Vol. 36, No. 4, 04022063.

Powers, D.M.W. (2011). “Evaluation: From precision, recall and F- measure to ROC, informedness, markedness and correlation.” International Journal of Machine Learning Technology, Vol. 2, No. 1, pp. 37-63.

Sim, G.-H., Jeong, H.-C., Hwang, D.-H., and Kim, D.-S. (2023). “Estimating Nakdong Estuary Barrage outflow using upstream hydrograph.” Journal of Korea Water Resources Association, Vol. 56, No. 3, pp. 165-171.

Wang, Q.-A., Lu, A.-W., Ni, Y.-Q., Wang, J.-F., and Ma, Z.-G. (2025). “Bayesian network in structural health monitoring: Theoretical background and applications review.” Sensors, Vol. 25, No. 12, 3577.