Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling-Reference-Cited by-同舟云学术

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Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling

Published:2018-09-05 Issue:1 Volume:33 Page:229-243
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Kasiviswanathan K. S.,He Jianxun,Tay Joo-Hwa,Sudheer K. P.

Funder

Unviersity of Calgary

Publisher

Springer Science and Business Media LLC

Subject

Water Science and Technology,Civil and Structural Engineering

Link

http://link.springer.com/article/10.1007/s11269-018-2099-x/fulltext.html

Reference20 articles.

1. Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013

2. Alvisi S, Creaco E, Franchini M (2012) Crisp discharge forecasts and grey uncertainty bands using data-driven models. Hydrol Res 43(5):589–602. https://doi.org/10.2166/nh.2012.121

3. Asefa T (2009) Ensemble streamflow forecast: a glue-based neural network approach. J Am Water Resour Assoc 45:1155–1163. https://doi.org/10.1111/j.1752-1688.2009.00351.x

4. Benaouda D, Murtagh F, Starck JL, Renaud O (2006) Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting. Neurocomputing 70:139–154. https://doi.org/10.1016/j.neucom.2006.04.005

5. Creaco E, Berardi L, Sun S, Giustolisi O, Savic D (2016) Selection of relevant input variables in storm water quality modeling by multiobjective evolutionary polynomial regression paradigm. Water Resour Res 52:2403–2419. https://doi.org/10.1002/2015WR017971

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