A Poisson-Gamma Model for Zero Inflated Rainfall Data
Rainfall modeling is significant for prediction and forecasting purposes in agriculture, weather derivatives, hydrology, and risk and disaster preparedness. Normally two models are used to model the rainfall process as a chain dependent process representing the occurrence and intensity of rainfall....
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2018/1012647 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849306875620753408 |
|---|---|
| author | Nelson Christopher Dzupire Philip Ngare Leo Odongo |
| author_facet | Nelson Christopher Dzupire Philip Ngare Leo Odongo |
| author_sort | Nelson Christopher Dzupire |
| collection | DOAJ |
| description | Rainfall modeling is significant for prediction and forecasting purposes in agriculture, weather derivatives, hydrology, and risk and disaster preparedness. Normally two models are used to model the rainfall process as a chain dependent process representing the occurrence and intensity of rainfall. Such two models help in understanding the physical features and dynamics of rainfall process. However rainfall data is zero inflated and exhibits overdispersion which is always underestimated by such models. In this study we have modeled the two processes simultaneously as a compound Poisson process. The rainfall events are modeled as a Poisson process while the intensity of each rainfall event is Gamma distributed. We minimize overdispersion by introducing the dispersion parameter in the model implemented through Tweedie distributions. Simulated rainfall data from the model shows a resemblance of the actual rainfall data in terms of seasonal variation, means, variance, and magnitude. The model also provides mechanisms for small but important properties of the rainfall process. The model developed can be used in forecasting and predicting rainfall amounts and occurrences which is important in weather derivatives, agriculture, hydrology, and prediction of drought and flood occurrences. |
| format | Article |
| id | doaj-art-20ff7c3a5a3e41e29390b1647fe210e0 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-20ff7c3a5a3e41e29390b1647fe210e02025-08-20T03:54:57ZengWileyJournal of Probability and Statistics1687-952X1687-95382018-01-01201810.1155/2018/10126471012647A Poisson-Gamma Model for Zero Inflated Rainfall DataNelson Christopher Dzupire0Philip Ngare1Leo Odongo2Pan African University Institute of Basic Sciences, Technology and Innovation, Juja, KenyaPan African University Institute of Basic Sciences, Technology and Innovation, Juja, KenyaPan African University Institute of Basic Sciences, Technology and Innovation, Juja, KenyaRainfall modeling is significant for prediction and forecasting purposes in agriculture, weather derivatives, hydrology, and risk and disaster preparedness. Normally two models are used to model the rainfall process as a chain dependent process representing the occurrence and intensity of rainfall. Such two models help in understanding the physical features and dynamics of rainfall process. However rainfall data is zero inflated and exhibits overdispersion which is always underestimated by such models. In this study we have modeled the two processes simultaneously as a compound Poisson process. The rainfall events are modeled as a Poisson process while the intensity of each rainfall event is Gamma distributed. We minimize overdispersion by introducing the dispersion parameter in the model implemented through Tweedie distributions. Simulated rainfall data from the model shows a resemblance of the actual rainfall data in terms of seasonal variation, means, variance, and magnitude. The model also provides mechanisms for small but important properties of the rainfall process. The model developed can be used in forecasting and predicting rainfall amounts and occurrences which is important in weather derivatives, agriculture, hydrology, and prediction of drought and flood occurrences.http://dx.doi.org/10.1155/2018/1012647 |
| spellingShingle | Nelson Christopher Dzupire Philip Ngare Leo Odongo A Poisson-Gamma Model for Zero Inflated Rainfall Data Journal of Probability and Statistics |
| title | A Poisson-Gamma Model for Zero Inflated Rainfall Data |
| title_full | A Poisson-Gamma Model for Zero Inflated Rainfall Data |
| title_fullStr | A Poisson-Gamma Model for Zero Inflated Rainfall Data |
| title_full_unstemmed | A Poisson-Gamma Model for Zero Inflated Rainfall Data |
| title_short | A Poisson-Gamma Model for Zero Inflated Rainfall Data |
| title_sort | poisson gamma model for zero inflated rainfall data |
| url | http://dx.doi.org/10.1155/2018/1012647 |
| work_keys_str_mv | AT nelsonchristopherdzupire apoissongammamodelforzeroinflatedrainfalldata AT philipngare apoissongammamodelforzeroinflatedrainfalldata AT leoodongo apoissongammamodelforzeroinflatedrainfalldata AT nelsonchristopherdzupire poissongammamodelforzeroinflatedrainfalldata AT philipngare poissongammamodelforzeroinflatedrainfalldata AT leoodongo poissongammamodelforzeroinflatedrainfalldata |