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Title: Uncertainty analysis on neural network based hydrological models using probabilistic point estimate method
Authors: Kasiviswanathan, Kasiapillai S.
Sudheer K.P.
Published in: Advances in Intelligent and Soft Computing
International Conference on Soft Computing for Problem Solving, SocProS 2011
Abstract: Modeling hydrological processes are always a challenge due to incomplete understanding of the physics of the process. Therefore, various levels of simplification are essential during modeling, which are otherwise very complex. In addition, most of the hydrological processes being natural are random processes. Apart from the standard physics based models developed in hydrology, the artificial neural network (ANN) approach has been getting lot of attention plausibly due to the complexity associated with the system. However, in most of the application of ANN in hydrology the model is considered as deterministic despite a large amount uncertainty associated with the final ANN models. Very recently, there has been considerable interest to quantify the uncertainty associated with ANN models, and not much work is reported since application of standard methods for uncertainty quantification was difficult due to the parallel computing architecture of the ANN. This paper presents the application of probabilistic point estimate in quantifying the uncertainty of ANN river flow forecasting model. The method is demonstrated through a case study of L'Anguille watershed located in United States. The results show that the method effectively quantifies uncertainty in the model output by estimating the parameters in orthogonal domain. The study also suggests that the method can be employed for models with lesser number of simulations, and do not require much knowledge about the parametric distribution of the model. © 2012 Springer India Pvt. Ltd.
Citation: Advances in Intelligent and Soft Computing (2012), 130 AISC(VOL. 1): 377-384
Issue Date: 2012
Keywords: Orthogonal domain
parallel computing
physics based models
random processes
ISBN: 9.78813E+12
ISSN: 18675662
Author Scopus IDs: 57212999476
Author Affiliations: Kasiviswanathan, K.S., Department of Civil Engineering, Indian Institute of Technology, Madras, Chennai 600036, India
Sudheer, K.P., Department of Civil Engineering, Indian Institute of Technology, Madras, Chennai 600036, India
Corresponding Author: Kasiviswanathan, K.S.; Department of Civil Engineering, , Madras, Chennai 600036, India; email:
Appears in Collections:Conference Publications [WR]

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