Bayesian Markov Chain Monte Carlo-based copulas: factoring the role of large-scale climate indices in monthly flood prediction
Edited book (chapter)
Chapter Title | Bayesian Markov Chain Monte Carlo-based copulas: factoring the role of large-scale climate indices in monthly flood prediction |
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Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 3337 |
Book Title | Intelligent data analytics for decision-support systems in hazard mitigation: theory and practice of hazard mitigation |
Authors | Nguyen-Huy, Thong (Author), Deo, Ravinesh C. (Author), Yaseen, Zaher Mundher (Author), Mushtaq, Shahbaz (Author) and Prasad, Ramendra (Author) |
Editors | Deo, Ravinesh C., Samui, Pijush, Kisi, Ozgur and Yaseen, Zaher Mundher |
Page Range | 29-47 |
Series | Springer Transactions in Civil and Environmental Engineering |
Chapter Number | 2 |
Number of Pages | 19 |
Year | 2021 |
Publisher | Springer |
Place of Publication | Singapore |
ISBN | 9789811557712 |
9789811557729 | |
ISSN | 2363-7633 |
2363-7641 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-15-5772-9_2 |
Web Address (URL) | https://link.springer.com/chapter/10.1007/978-981-15-5772-9_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. |
Keywords | copula; flood index; climate index; monthly prediction; nonlinear modeling |
ANZSRC Field of Research 2020 | 410402. Environmental assessment and monitoring |
460207. Modelling and simulation | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Centre for Applied Climate Sciences |
School of Sciences | |
Ton Duc Thang University, Vietnam | |
University of Fiji, Fiji | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5wqq/bayesian-markov-chain-monte-carlo-based-copulas-factoring-the-role-of-large-scale-climate-indices-in-monthly-flood-prediction
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