References
- 김태영 (2017) 블록과 함께하는 파이썬 딥러닝 케라스.
- 신병호 (2018) 광역상수도 관로사고 대응을 위한 배수구간 적정 설계기법 개발, 박사학위논문, 충남대학교
- Jung, D., Kang, D., Liu, J., & Lansey, K. (2015). Improving the rapidity of responses to pipe burst in water distribution systems: a comparison of statistical process control methods. Journal of Hydroinformatics, 17(2), 307-328. https://doi.org/10.2166/hydro.2014.101
- Loureiro, D., Amado, C., Martins, A., Vitorino, D., Mamade, A., & Coelho, S. T. (2016). Water distribution systems flow monitoring and anomalous event detection: a practical approach. Urban Water Journal, 13(3), 242-252. https://doi.org/10.1080/1573062X.2014.988733
- Mounce, S. R., Day, A. J., Wood, A. S., Khan, A., Widdop, P. D., & Machell, J. (2002). A neural network approach to burst detection. Water science and technology, 45(4-5), 237-246.
- Mounce, S. R., Khan, A., Wood, A. S., Day, A. J., Widdop, P. D., & Machell, J. (2003). Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system. Information Fusion, 4(3), 217-229. https://doi.org/10.1016/S1566-2535(03)00034-4
- Mounce, S. R., Boxall, J. B., & Machell, J. (2009). Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows. Journal of Water Resources Planning and Management, 136(3), 309-318. https://doi.org/10.1061/(ASCE)WR.1943-5452.000003
- Mounce, S. R., Mounce, R. B., & Boxall, J. B. (2011). Novelty detection for time series data analysis in water distribution systems using support vector machines. Journal of hydroinformatics, 13(4), 672-686. https://doi.org/10.2166/hydro.2010.144
- Mounce, S. R., & Machell, J. (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. https://doi.org/10.1080/15730620600578538
- Romano, M., Kapelan, Z. and Savic, D.A., 2014a. Automated detection of pipe bursts and other events in water distribution systems. Journal of Water Resources Planning and Management, 140 (4), 457-467. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000339
- Romano, M., Kapelan, Z. and Savic, D.A., 2014b. Evolutionary algorithm and expectation maximization strategies for improved detection of pipe bursts and other events in water distribution systems. Journal of Water Resources Planning and Management, 140 (5), 572-584. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000347
- Rossman (2000) EPANET2 User's Manual, EPA
- WHO (2002) DISASTERS & EMERGENCIES DEFINITIONS. Training Package, Panafrican Emergency Training Centre, Addis Ababa.
- Wikipedia (2018a) 2018.11.15. 접속기준, https://en.wikipedia.org/wiki/Crisis
- Wikipedia (2018b) 2018.11.15. 접속기준, https://ko.wikipedia.org/wiki/%EC%88%98%EC%8B%A0%EC%9E%90_%EC%A1%B0%EC%9E%91_%ED%8A%B9%EC%84%B1
- Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82. https://doi.org/10.1109/4235.585893
- Wu, Y., & Liu, S. (2017). A review of data-driven approaches for burst detection in water distribution systems. Urban Water Journal, 14(9), 972-983. https://doi.org/10.1080/1573062X.2017.1279191
- Ye, G.L. and Fenner, R.A., 2011. Kalman filtering of hydraulic measurements for burst detection in water distribution systems. Journal of Pipeline Systems Engineering and Practice, 2 (1), 14-22. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000070