.png){kind=icon}
返回
Constructing multivariate distribution of rainfall characteristics: A Bayesian vine algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jhydrol.2024.131392.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Constructing a multivariate distribution model for rainfall characteristics is an important task for risk -based hydrologic design. However, most stochastic rainfall models are limited to bivariate frequentist distribution and cannot be easily generalized to multivariate ( >= 3 ) Bayesian settings. Towards this problem, this study presents a Bayesian vine algorithm. The presented algorithm translates the high -dimensional Bayesian update task into multiple lower -dimensional update tasks using the vine approach. The algorithm also computes the evidence, allowing Bayesian model selection of marginals and copulas in the joint distribution. Two numerical examples, a synthetic rainfall data from Canada, and a real rainfall data from China are utilized to demonstrate the algorithm's capability. The results suggest that the algorithm can satisfactorily identify and characterize the underlying marginal and dependence for the rainfall characteristics. Since the highdimensional Bayesian update task is translated into lower -dimensional Bayesian updates, the algorithm is scalable, stable, and computationally efficient in higher dimensions. A 20 -year continuous hourly rainfall record can be characterized for its depth, duration, and intensity in around 60 s without any convergence or tuning issues. Overall, this study provides a powerful Bayesian tool for constructing stochastic rainfall models, which in turn can be used in multiple civil engineering disciplines concerned with rainfall interaction. This study does not consider temporal/serial dependence but only the cross -dependence among the rainfall characteristics.
Keyword:
Rainfall model
Bayesian modelling
multivariate distribution
Vine copula
Bayesian model selection
期刊
IF:
6.3
论文数:
2.4W
被引数:
9.8W
机构
引用论文
Transitional markov chain monte carlo method for Bayesian model updating, model class selection, and model averaging用于贝叶斯模型更新，模型类选择和模型平均的过渡马尔可夫链蒙特卡洛方法
PREAI
分享收藏
Time-varying copula and average annual reliability-based nonstationary hazard assessment of extreme rainfall events.png)
JOURNAL OF HYDROLOGY
JOURNAL OF HYDROLOGYIF6.3
PREAI
分享收藏Developing an entropy and copula-based approach for precipitation monitoring network expansionJOURNAL OF HYDROLOGY
JOURNAL OF HYDROLOGYIF6.3
PREAI
分享收藏
查看更多内容