Monthly rainfall forecasting with Markov Chain Monte Carlo simulations integrated with statistical bivariate copulas
Edited book (chapter)
Chapter Title | Monthly rainfall forecasting with Markov Chain Monte Carlo simulations integrated with statistical bivariate copulas |
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Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 1821 |
Book Title | Handbook of probabilistic models |
Authors | Ali, Mumtaz (Author), Deo, Ravinesh C. (Author), Downs, Nathan J. (Author) and Maraseni, Tek (Author) |
Editors | Samui, Pijush, Bui, Dieu Tien, Chakraborty, Subrata and Deo, Ravinesh C. |
Page Range | 89-105 |
Chapter Number | 3 |
Number of Pages | 17 |
Year | 2020 |
Publisher | Elsevier |
Place of Publication | Oxford, United Kingdom |
ISBN | 9780128165140 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/B9780128165140000035 |
Abstract | Probabilistic models used for forecasting rainfall can help stakeholders in improving crop productivity through better utilization and preplanning of water resources, as it is the crucial element of major decisions because of the dynamic nature of climate phenomena. In this chapter, Markov Chain Monte Carlo simulation technique was integrated with statistical bivariate copulas to develop rainfall forecasting models by incorporating antecedent rainfall significant lag (t-1) as a predictor to forecast rainfall of the preceding month in Peshawar, Pakistan. Twenty-five copula models were developed using some well-know copula families (Gaussian, t, Clayton, Gumble Frank and Fischer-Hinzmann etc.) to sort out the optimal model using Akaike information criterion (AIC), Bayesian information criterion (BIC), and maximum likelihood (MaxL) as base criteria for each region. The Markov Chain Monte Carlo (MCMC)-bivariate Farlie-Gumbel-Morgenstern copula attained the highest values of AIC ≈ -4167.1, Bayesian Information Criterion ≈ -4163.1, and MaxL ≈ 2084.5. |
Keywords | Markov chain; Monte Carlo based copula; model; rainfall; rainfall forecasting |
ANZSRC Field of Research 2020 | 410499. Environmental management not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q56q3/monthly-rainfall-forecasting-with-markov-chain-monte-carlo-simulations-integrated-with-statistical-bivariate-copulas
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