Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models-Reference-Cited by-同舟云学术

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Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models

Published:2016-12-08 Issue:7 Volume:31 Page:1659-1670
ISSN:1436-3240
Container-title:Stochastic Environmental Research and Risk Assessment
language:en
Short-container-title:Stoch Environ Res Risk Assess

Author:

Kasiviswanathan K. S.,Sudheer K. P.

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science,Safety, Risk, Reliability and Quality,Water Science and Technology,Environmental Chemistry,Environmental Engineering

Link

http://link.springer.com/article/10.1007/s00477-016-1369-5/fulltext.html

Reference64 articles.

1. Abrahart RJ, See LM, Dawson CW, Shamseldin AY, Wilby RL (2010) Nearly two decades of neural network hydrologic modeling. In: Sivakumar B, Berndtsson R (eds) Advances in data-based approaches for hydrologic modeling and forecasting. World Scientific Publishing, Hackensack, NJ, pp 267–346

2. Alvisi S, Franchini M (2011) Fuzzy neural networks for water level and discharge forecasting with uncertainty. Environ Model Softw 26:523–537. doi: 10.1016/j.envsoft.2010.10.016

3. Alvisi S, Franchini M (2012) Grey neural networks for river stage forecasting with uncertainty. Phys Chem Earth Parts A/B/C 42–44:108–118. doi: 10.1016/j.pce.2011.04.002

4. Araghinejad S, Azmi M, Kholghi M (2011) Application of artificial neural network ensembles in probabilistic hydrological forecasting. J Hydrol 407:94–104. doi: 10.1016/j.jhydrol.2011.07.011

5. Artigue G, Johannet A, Borrell V, Pistre S (2012) Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France). Nat Hazards Earth Syst Sci 12:3307–3324. doi: 10.5194/nhess-12-3307-2012

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