Open Access
Issue
MATEC Web Conf.
Volume 147, 2018
The Third International Conference on Sustainable Infrastructure and Built Environment (SIBE 2017)
Article Number 03014
Number of page(s) 3
Section Water Resources Engineering and Management
DOI https://doi.org/10.1051/matecconf/201814703014
Published online 22 January 2018
  1. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial Neural Networks in hydrology I: Preliminary Concepts, Journal of Hydrologic Engineering 5, 115–123 (2000). [Google Scholar]
  2. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, Artificial Neural Networks in hydrology II: Hydrological Applications, Journal of Hydrologic Engineering 5, 124–137 (2000). [Google Scholar]
  3. L.C. Chang, H.Y. Shen, Y.F. Wang, J.Y. Huang, Y.T. Lin, Clustering-based hybrid inundation model for forecasting flood inundation depths, Journal of Hydrology 385, 257–268 (2010). [CrossRef] [Google Scholar]
  4. W.C. Hong, Rainfall Forecasting by Technological Machine Learning Models, Applied Mathematics and Computation 200, 41–57 (2008). [CrossRef] [Google Scholar]
  5. G.F Lin, Y.C. Chou, M.C. Wu, Typhoon flood forecasting using integrated two-stage support vector machine approach, Journal of Hydrology 486, 334–342 (2013). [CrossRef] [Google Scholar]
  6. H.R. Maier, G.C. Dandy, Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications, Environmental Modelling and Software 15, 101–124 (2000). [Google Scholar]
  7. T.Y. Pan, J.S. Lai, T.J. Chang, H.K. Chang, K.C. Chang, Y.C. Tan, Hybrid neural networks in rainfall-inundation forecasting based on a synthetic potential inundation database, Natural Hazards and Earth System Sciences 11, 771–787 (2011). [CrossRef] [Google Scholar]

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