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| Titel |
Bayesian neural network modeling of tree-ring temperature variability record from the Western Himalayas |
| VerfasserIn |
R. K. Tiwari, S. Maiti |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 18, no. 4 ; Nr. 18, no. 4 (2011-08-05), S.515-528 |
| Datensatznummer |
250013947
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| Publikation (Nr.) |
 copernicus.org/npg-18-515-2011.pdf |
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| Zusammenfassung |
| A novel technique based on the Bayesian neural network (BNN) theory is
developed and employed to model the temperature variation record from the
Western Himalayas. In order to estimate an a posteriori probability function, the BNN is
trained with the Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC)
simulations algorithm. The efficacy of the new algorithm is tested on the
well known chaotic, first order autoregressive (AR) and random models and
then applied to model the temperature variation record decoded from the
tree-ring widths of the Western Himalayas for the period spanning over
1226–2000 AD. For modeling the actual tree-ring temperature data, optimum
network parameters are chosen appropriately and then cross-validation test
is performed to ensure the generalization skill of the network on the new
data set. Finally, prediction result based on the BNN model is compared with
the conventional artificial neural network (ANN) and the AR linear models
results. The comparative results show that the BNN based analysis makes
better prediction than the ANN and the AR models. The new BNN modeling
approach provides a viable tool for climate studies and could also be
exploited for modeling other kinds of environmental data. |
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