Open Access
MATEC Web Conf.
Volume 246, 2018
2018 International Symposium on Water System Operations (ISWSO 2018)
Article Number 02039
Number of page(s) 7
Section Parallel Session I: Water Resources System
Published online 07 December 2018
  1. Lima, Carlos HR, and Upmanu Lall. Climate informed long term seasonal forecasts of hydroenergy inflow for the Brazilian hydropower system. Journal of hydrology, 381(1-2): 65-75. (2010) [CrossRef] [Google Scholar]
  2. Soukup T L., Aziz O A., Tootle G A., Piechota T C., & Wulff S S. Long lead-time streamflow forecasting of the North Platte River incorporating oceanic-atmospheric climate variability. Journal of Hydrology, 368(1-4), 131-142. (2009) [CrossRef] [Google Scholar]
  3. F Gutierrez, J A Dracup. An analysis of the feasibility of long-range streamflow forecasting for Colombia using El Nino-Southern Oscillation indicators[J]. Journal of Hydrology, 246: 181-196. (2001) [CrossRef] [Google Scholar]
  4. H S Yan, X D Yan, Impact of the Preceding Northern Hemisphere 500hPa Geopotential Height and Pacific SST Variation on the Flood Season Precipitation over China[J], Chinese Journal of Atmospheric Sciences, 28 (3): 405-413 (2004) [Google Scholar]
  5. Araghinejad, Shahab, Donald H. Burn, and Mohammad Karamouz. Long-lead probabilistic forecasting of streamflow using ocean-atmospheric and hydrological predictors. Water Resources Research. 42(3) (2006). [Google Scholar]
  6. W C Wang, K W Chau, C T Cheng, et al. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series[J]. Journal of hydrology, 374(3-4): 294-306. (2009) [Google Scholar]
  7. GLENN A T, ASHOK K S, THOMAS C P, et, al. Long Lead-Time Forecasting of U. S. Streamflow Using Partial Least Squares Regression[J]. Journal of Hydrologic Engineering, 12 (5): 442-451. (2007) [CrossRef] [Google Scholar]
  8. M Zhang, C J Li, Y C Zhang, Application of the Bayesian statistic hydrological forecast system to middle-and long-term runoff forecast[J], Advances in Water Science, 20 (1): 40-44, (2009) [Google Scholar]
  9. Pawlak Z, Polkowski L, Skowron A. Rough Set Theory[J]. (2008) [Google Scholar]
  10. Pawlak Z. Rough set theory and its applications to data analysis[J]. Cybernetics & Systems, 29 (7): 661-688. (1998) [CrossRef] [Google Scholar]
  11. T Y Lin, Q Liu. Rough approximate operators: axiomatic rough set theory[M]//Rough Sets, Fuzzy Sets and Knowledge Discovery. Springer, London, 256-260 (1994) [CrossRef] [Google Scholar]
  12. Tipping M E. The relevance vector machine[C]//Advances in neural information processing systems. 652-658. (2000) [Google Scholar]
  13. Tipping M E. Sparse Bayesian learning and the relevance vector machine[J]. Journal of machine learning research, 1(Jun): 211-244. (2001) [Google Scholar]
  14. Cortes C, Vapnik V. Support-vector networks[J]. Machine learning, 20 (3): 273-297. (1995) [Google Scholar]
  15. J Y Lin, C T Cheng. Application of support vector machine method to long-term runoff forecast[J]. Journal of Hydraulic Engineering, 37 (6): 681-686. (2006) [Google Scholar]
  16. G R Yu, Z Q Xia. Prediction model of chaotic time series based on support vector machine and its application to runoff[J]. Advances in Water Science, 19 (1): 116-122. (2008) [Google Scholar]
  17. W T Deng, Z B Sun, G Zeng, et al. Interdecadal variation of summer precipitation pattern over eastern China and its relationship with the North Pacific SST [J]. Chinese Journal of Atmospheric Sciences, 33 (4): 835-846. (2009) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.