Open Access
Issue
E3S Web Conf.
Volume 350, 2022
International Conference on Environment, Renewable Energy and Green Chemical Engineering (EREGCE 2022)
Article Number 01029
Number of page(s) 5
Section Green Chemical Engineering
DOI https://doi.org/10.1051/e3sconf/202235001029
Published online 09 May 2022
  1. S Grimaldi, Y. Li, J. P. Walker, V. R. N. Pauwels. Effective representation of river geometry in hydraulic flood forecast models. Water Resour Res. 54, 2: 1031-1057.(2018) [Google Scholar]
  2. S Han, P Coulibaly, Bayesian flood forecasting methods: A review. J. Hydrol. 551a: 340-351. (2017) [CrossRef] [Google Scholar]
  3. J Karsten, J Gurtz, H Lang. Advanced flood forecasting in Alpine watersheds by coupling meteorological observations and forecasts with a distributed hydrological model. J. Hydrol. 267: 4052. (2002) [Google Scholar]
  4. H.O. Sharif, L Sparks, A.A. Hassan., J Zeitler, H Xie, Application of a distributed hydrologic model to the November 17, 2004, flood of Bull Creek watershed, Austin, Texas. J. Hydrol. Eng. 15, 8: 651-657. (2010) [Google Scholar]
  5. L.C. Chang, M.Z.M. Amin, S.N. Yang, F.J. Chang, Building ANN-based regional multi-step-ahead flood inundation forecast models. Water. 10, 9: 1283. (2018) [Google Scholar]
  6. H Shi, T Li, R Liu, J Chen, J Li, A Zhang, G Wang. A service-oriented architecture for ensemble flood forecast from numerical weather prediction. J. Hydrol. 527: 933-942. (2015) [CrossRef] [Google Scholar]
  7. G Jean-Pierre, M Gleizes, P Glize, C Régis. Realtime simulation for flood forecast: an adaptive multi-agent system staff. In Proceedings of the AISB. Aberystwyth. 109-114. (2003) [Google Scholar]
  8. K Roman. The case for probabilistic forecasting in hydrology. J. Hydrol. 249, 2-9.. (2001) [CrossRef] [Google Scholar]
  9. H.L. Cloke, F Pappenberger. Ensemble flood forecasting: A review. J. Hydrol. 375: 613-626. (2009) [CrossRef] [Google Scholar]
  10. R Zhao. The Xinanjiang model applied in China. J. Hydrol. 135: 371-381. (1992) [CrossRef] [Google Scholar]
  11. K James, R Eberhart. Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural network. Perth. 1942-1948. (1995) [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.