Bayesian Markov Chain Monte Carlo-based copulas: factoring the role of large-scale climate indices in monthly flood prediction

Nguyen-Huy, Thong ORCID: https://orcid.org/0000-0002-2201-6666 and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Yaseen, Zaher Mundher and Mushtaq, Shahbaz and Prasad, Ramendra (2021) Bayesian Markov Chain Monte Carlo-based copulas: factoring the role of large-scale climate indices in monthly flood prediction. In: Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation. Springer Transactions in Civil and Environmental Engineering. Springer, Singapore, pp. 29-47. ISBN 978-981-15-5771-2


Abstract

Floods are caused by heavy rainfall associated with variation of large-scale climate index, El Niño–Southern Oscillation (ENSO). The chapter applies an advanced statistical copula approach to model lag relationships between monthly Southern Oscillation Index (SOI), an ENSO indicator, and monthly Flood Index (FI) that can be used for flood prediction. Copula parameters were numerically derived from under a hybrid-evolution Markov chain Monte Carlo (MCMC) approach within a Bayesian framework. The empirical findings showed that monthly SOI data from Aug to Dec have a significant correlation with monthly FI that can be predicted at least four months ahead using SOI information. These advanced flood prediction models, presented in this chapter, are indeed imperative tools for civil protection and important to early warning and risk reduction systems.


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Item Type: Book Chapter (Commonwealth Reporting Category B)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published chapter in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Institute for Life Sciences and the Environment - Centre for Applied Climate Sciences (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 02 Sep 2020 00:17
Last Modified: 02 Sep 2020 02:20
Uncontrolled Keywords: copula; flood index; climate index; monthly prediction; nonlinear modeling
Fields of Research (2008): 05 Environmental Sciences > 0502 Environmental Science and Management > 050204 Environmental Impact Assessment
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: https://doi.org/10.1007/978-981-15-5772-9_2
URI: http://eprints.usq.edu.au/id/eprint/39213

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