Journal
JOURNAL OF HYDROLOGY
Volume 409, Issue 3-4, Pages 696-709Publisher
ELSEVIER
DOI: 10.1016/j.jhydrol.2011.09.002
Keywords
Bayesian Neural Networks; Evolutionary Monte Carlo; Hydrologic forecasting; Streamflow; Uncertainty
Funding
- DOE Great Lakes Bioenergy Research Center (DOE BER Office of Science) [DE-FC02-07ER64494]
- DOE BER Office of Science [KP1601050]
- DOE EERE [OBP 2046919145]
- National Science Foundation [DMS-0607755, CMMI-0926803]
- King Abdullah University of Science and Technology (KAUST) [KUS-C1-016-04]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1007457] Funding Source: National Science Foundation
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Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework (BNN-PIS) to incorporate the uncertainties associated with parameters, inputs, and structures into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform BNNs that only consider uncertainties associated with parameters and model structures. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters shows that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of and interactions among different uncertainty sources is expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting. (C) 2011 Elsevier B.V. All rights reserved.
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