Complementary Modeling Approach for Estimating Sedimentation and Hydraulic Flushing Parameters Using Artificial Neural Networks and RESCON2 Model
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s12205-021-1877-9.pdf
Reference60 articles.
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2. ASCE Task Committee (2000b) Artificial neural networks in hydrology. II: Preliminary hydrologic applications. Journal of Hydrologic Engineering 5(2):124–137, DOI: https://doi.org/10.1061/(ASCE)1084-0699(2000)5:2(124)
3. Atkinson E (1996) The feasibility of flushing sediment from reservoirs. Report OD 137, TDR Project R5839, HR Wallingford, Oxen, UK
4. Bharti B, Pandey A, Tripathi SK, Kumar D (2017) Modelling of runoff and sediment yield using ANN, LS-SVR, REPTree and M5 models. Hydrology Research 48(6):1489–1507, DOI: https://doi.org/10.2166/nh.2017.153
5. Bussi G, Dadson SJ, Bowes MJ, Whitehead PG (2017) Seasonal and interannual changes in sediment transport identified through sediment rating curves. Journal of Hydrologic Engineering 22(2):06016016, DOI: https://doi.org/10.1061/(ASCE)HE.1943-5584.0001466
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