Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland
This study investigates the spatial heterogeneity in the maximum monthly rainfall amounts reported by stations in Ireland from January 2018 to December 2020. The heterogeneity is modeled by the Bayesian normal mixture model with different ranks. The selection of the best model or the degree of heter...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-03-01
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| Series: | Open Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/eng-2022-0024 |
| _version_ | 1811203689174532096 |
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| author | Hussein Amjad Kadhem Safaa K. |
| author_facet | Hussein Amjad Kadhem Safaa K. |
| author_sort | Hussein Amjad |
| collection | DOAJ |
| description | This study investigates the spatial heterogeneity in the maximum monthly rainfall amounts reported by stations in Ireland from January 2018 to December 2020. The heterogeneity is modeled by the Bayesian normal mixture model with different ranks. The selection of the best model or the degree of heterogeneity is implemented using four criteria which are the modified Akaike information criterion, the modified Bayesian information criterion, the deviance information criterion, and the widely applicable information criterion. The estimation and model selection process is implemented using the Gibbs sampling. The results show that the maximum monthly rainfall amounts are accommodated in two and three components. The goodness of fit for the selected models is checked using the graphical plots including the probability density function and cumulative distribution function. This article also contributes via the spatial determination of return level or rainfall amounts at risk with different return periods using the prediction intervals constructed from the posterior predictive distribution. |
| first_indexed | 2024-04-12T02:59:49Z |
| format | Article |
| id | doaj.art-bb6609334d644fc4a433d35103acc11a |
| institution | Directory Open Access Journal |
| issn | 2391-5439 |
| language | English |
| last_indexed | 2024-04-12T02:59:49Z |
| publishDate | 2022-03-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Engineering |
| spelling | doaj.art-bb6609334d644fc4a433d35103acc11a2022-12-22T03:50:42ZengDe GruyterOpen Engineering2391-54392022-03-0112120421410.1515/eng-2022-0024Spatial mixture modeling for analyzing a rainfall pattern: A case study in IrelandHussein Amjad0Kadhem Safaa K.1Department of Civil Engineering, College of Engineering, Al-Muthanna University Al-Muthanna, Samawah, IraqDepartment of Mathematics and Computer Applications, College of Science, Al-Muthanna University, Samawah, IraqThis study investigates the spatial heterogeneity in the maximum monthly rainfall amounts reported by stations in Ireland from January 2018 to December 2020. The heterogeneity is modeled by the Bayesian normal mixture model with different ranks. The selection of the best model or the degree of heterogeneity is implemented using four criteria which are the modified Akaike information criterion, the modified Bayesian information criterion, the deviance information criterion, and the widely applicable information criterion. The estimation and model selection process is implemented using the Gibbs sampling. The results show that the maximum monthly rainfall amounts are accommodated in two and three components. The goodness of fit for the selected models is checked using the graphical plots including the probability density function and cumulative distribution function. This article also contributes via the spatial determination of return level or rainfall amounts at risk with different return periods using the prediction intervals constructed from the posterior predictive distribution.https://doi.org/10.1515/eng-2022-0024rainfall amountsbayesian mixture modelingcumulative distributionprediction intervalposterior predictive distribution |
| spellingShingle | Hussein Amjad Kadhem Safaa K. Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland Open Engineering rainfall amounts bayesian mixture modeling cumulative distribution prediction interval posterior predictive distribution |
| title | Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland |
| title_full | Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland |
| title_fullStr | Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland |
| title_full_unstemmed | Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland |
| title_short | Spatial mixture modeling for analyzing a rainfall pattern: A case study in Ireland |
| title_sort | spatial mixture modeling for analyzing a rainfall pattern a case study in ireland |
| topic | rainfall amounts bayesian mixture modeling cumulative distribution prediction interval posterior predictive distribution |
| url | https://doi.org/10.1515/eng-2022-0024 |
| work_keys_str_mv | AT husseinamjad spatialmixturemodelingforanalyzingarainfallpatternacasestudyinireland AT kadhemsafaak spatialmixturemodelingforanalyzingarainfallpatternacasestudyinireland |