Journal
PEERJ
Volume 10, Issue -, Pages -Publisher
PEERJ INC
DOI: 10.7717/peerj.13465
Keywords
Credible intervals; Highest posterior density intervals; Jeffrey?s rule; Uniform priors; Fiducial quantities; Chiang Mai; Simulation; Rainfall data
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Funding
- National Science, Research, and Innovation Fund (NSRF)
- King Mongkut's University of Technology North Bangkok [KMUTNB-FF-66-03]
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This study constructs credible and highest posterior density (HPD) intervals for rainfall forecasting based on Bayesian methods, providing a way to predict future rainfall and reduce the risks of disaster caused by excessive or too little rainfall. The proposed methods are illustrated using rainfall data from Chiang Mai province, Thailand.
Precipitation and flood forecasting are difficult due to rainfall variability. The mean of a delta-gamma distribution can be used to analyze rainfall data for predicting future rainfall, thereby reducing the risks of future disasters due to excessive or too little rainfall. In this study, we construct credible and highest posterior density (HPD) intervals for the mean and the difference between the means of delta-gamma distributions by using Bayesian methods based on Jeffrey's rule and uniform priors along with a confidence interval based on fiducial quantities. The results of a simulation study indicate that the Bayesian HPD interval based on Jeffrey's rule prior performed well in terms of coverage probability and provided the shortest expected length. Rainfall data from Chiang Mai province, Thailand, are also used to illustrate the efficacies of the proposed methods.
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