Monthly rainfall forecasting with Markov Chain Monte Carlo simulations integrated with statistical bivariate copulas

Ali, Mumtaz and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Downs, Nathan J. ORCID: https://orcid.org/0000-0002-3191-6404 and Maraseni, Tek (2020) Monthly rainfall forecasting with Markov Chain Monte Carlo simulations integrated with statistical bivariate copulas. In: Handbook of probabilistic models. Elsevier (Butterworth-Heinemann), Oxford, United Kingdom, pp. 89-105. ISBN 978-0-12-816514-0


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.


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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 - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Date Deposited: 22 Oct 2019 23:33
Last Modified: 23 Oct 2019 01:46
Uncontrolled Keywords: Markov chain; Monte Carlo based copula; model; rainfall; rainfall forecasting
Fields of Research (2008): 05 Environmental Sciences > 0502 Environmental Science and Management > 050299 Environmental Science and Management not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
URI: http://eprints.usq.edu.au/id/eprint/37212

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