Open Access
Issue
MATEC Web Conf.
Volume 246, 2018
2018 International Symposium on Water System Operations (ISWSO 2018)
Article Number 01093
Number of page(s) 6
Section Main Session: Water System Operations
DOI https://doi.org/10.1051/matecconf/201824601093
Published online 07 December 2018
  1. Agrawal R, Faloutsos C, Swami A N. Efficient Similarity Search in Sequence Databases [C]. International Conference on Foundations of Data Organization and Algorithms. Springer-Verlag, 1993:69-84. [CrossRef] [Google Scholar]
  2. Aljawarneh S, Radhakrishna V, Kumar P V, et al. A similarity measure for temporal pattern discovery in time series data generated by IoT [C]. International Conference on Engineering & Mis. IEEE, 2016. [Google Scholar]
  3. Barbetta S, Coccia G, Moramarco T, et al. The multi temporal/multi-model approach to predictive uncertainty assessment in real-time flood forecasting [J]. Journal of Hydrology, 2017, 551. [Google Scholar]
  4. Ben Daoud, A., Sauquet, E., Lang, M., and Ramos, M.-H. (2011b). Can we extend flood forecastingleadtime by optimising precipitation forecasting based on analogs? Application to the Seine river basin. La Houille Blanche, (1):37–43. [Google Scholar]
  5. Berndt D J, Clifford J. Finding patterns in time series: a dynamic programming approach [M]. Advances in knowledge discovery and data mining. American Association for Artificial Intelligence, 1996:229-248. [Google Scholar]
  6. Casagrande L, Tomasella J, Alvalá R C D S, et al. Early flood warning in the Itajaí-Açu River basin using numerical weather forecasting and hydrological modeling [J]. Natural Hazards, 2017, 88(2):741-757. [CrossRef] [Google Scholar]
  7. Das G, Gunopulos D, Mannila H. Finding similar time series [C]. European Symposium on Principles of Data Mining and Knowledge Discovery. Springer, Berlin, Heidelberg, 1997:88-100. [CrossRef] [Google Scholar]
  8. Jamali S, Jönsson P, Eklundh L, et al. Detecting changes in vegetation trends using time series segmentation [J]. Remote Sensing of Environment, 2015:182-195. [CrossRef] [Google Scholar]
  9. Kehagias A, Petridis V. Time-Series Segmentation Using Predictive Modular Neural Networks [J]. Neural Computation, 1997, 9(8):1691-1709. [CrossRef] [Google Scholar]
  10. Keogh E J, Pazzani M J. A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases [J]. Computers & Education, 2000, 1805(3):122-133. [Google Scholar]
  11. Klatt, P. and Schultz, G. A., 1983. Flood forecasting on the basis of radar rainfall measurement and rainfall forecasting. [Google Scholar]
  12. Li B, Liang Z, Zhang J, et al. Risk Analysis of Reservoir Flood Routing Calculation Based on Inflow Forecast Uncertainty [J]. Water, 2016, 8(11):486. [CrossRef] [Google Scholar]
  13. Li J, Chen Y, Wang H, et al. Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model[J]. Hydrology & Earth System Sciences Discussions, 2017, 21:1-45. [CrossRef] [Google Scholar]
  14. Maciej Krawczak, Grażyna Szkatuła. An approach to dimensionality reduction in time series [J]. Information Sciences, 2014, 260(1):15-36. [CrossRef] [Google Scholar]
  15. Ouyang R, Ren L, Cheng W, et al. Similarity search and pattern discovery in hydrological time series data mining[J]. Hydrological Processes, 2010, 24(9):1198-1210. [CrossRef] [Google Scholar]
  16. Rubner Y., Tomasi C. and Guibas L.J., 2000. The Earth Mover’s Distance as a Metric for Image Retrieval. International Journal of Computer Vision, 40(2):99-121. [Google Scholar]
  17. Spate J M, Crokeb B F W, Jakemanb A J. Data Mining in Hydrology [J]. Hydrological Processes, 2003, 19(7):1511–1515. [Google Scholar]
  18. Solomatine, D., Dulal, K. Model trees as an alternative to neural networks in rainfall-runoff modelling. International Association of Scientific Hydrology Bulletin, 2003, 48(3),399-411. [Google Scholar]
  19. Vach é K B, Mcdonnell J J. A process ‐ based rejectionist framework for evaluating catchment runoff model structure[J]. Water Resources Research, 2006, 42(2):262-275. [Google Scholar]
  20. Veitzer S A, Gupta V K. Statistical self-similarity of width function maxima with implications to floods [J]. Advances in Water Resources, 2001, 24(9):955-965. [CrossRef] [Google Scholar]
  21. Wan X Y, Wang G Q, Peng Y, et al. Similarity-based optimal operation of water and sediment in a sediment-laden reservoir.[J]. Water Resources Management, 2010, 24(15):4381-4402. [CrossRef] [Google Scholar]
  22. Wang J, Shi P, Jiang P, et al. Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting[J]. Water, 2017, 9(1):48. [CrossRef] [Google Scholar]
  23. Wang Y, Guo S, Xiong L, et al. Daily Runoff Forecasting Model Based on ANN and Data Preprocessing Techniques[J]. Water, 2015, 2015(7):4144-4160. [CrossRef] [Google Scholar]

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