Parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis–Hastings algorithm

A new parameter optimization and uncertainty assessment procedure using the Bayesian inference with an adaptive Metropolis–Hastings (AM-H) algorithm is presented for extreme rainfall frequency modeling. An efficient Markov chain Monte Carlo sampler is adopted to explore the posterior distribution of...

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Autores principales:	Xingpo Liu, Chengfei Xia, Yifan Tang, Jiayang Tu, Huimin Wang
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	adaptive metropolis–hastings (am-h) algorithm frequency distribution markov chain monte carlo (mcmc) parameter optimization parameter uncertainty analysis rainfall frequency modeling Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/9d38a50752a74a68828bf7e3b4434d7a
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spelling	oai:doaj.org-article:9d38a50752a74a68828bf7e3b4434d7a2021-11-06T10:50:47ZParameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis–Hastings algorithm0273-12231996-973210.2166/wst.2021.032https://doaj.org/article/9d38a50752a74a68828bf7e3b4434d7a2021-03-01T00:00:00Zhttp://wst.iwaponline.com/content/83/5/1085https://doaj.org/toc/0273-1223https://doaj.org/toc/1996-9732A new parameter optimization and uncertainty assessment procedure using the Bayesian inference with an adaptive Metropolis–Hastings (AM-H) algorithm is presented for extreme rainfall frequency modeling. An efficient Markov chain Monte Carlo sampler is adopted to explore the posterior distribution of parameters and calculate their uncertainty intervals associated with the magnitude of estimated rainfall depth quantiles. Also, the efficiency of AM-H and conventional maximum likelihood estimation (MLE) in parameter estimation and uncertainty quantification are compared. And the procedure was implemented and discussed for the case of Chaohu city, China. Results of our work reveal that: (i) the adaptive Bayesian method, especially for return level associated to large return period, shows better estimated effect when compared with MLE; it should be noted that the implementation of MLE often produces overy optimistic results in the case of Chaohu city; (ii) AM-H algorithm is more reliable than MLE in terms of uncertainty quantification, and yields relatively narrow credible intervals for the quantile estimates to be instrumental in risk assessment of urban storm drainage planning.Xingpo LiuChengfei XiaYifan TangJiayang TuHuimin WangIWA Publishingarticleadaptive metropolis–hastings (am-h) algorithmfrequency distributionmarkov chain monte carlo (mcmc)parameter optimizationparameter uncertainty analysisrainfall frequency modelingEnvironmental technology. Sanitary engineeringTD1-1066ENWater Science and Technology, Vol 83, Iss 5, Pp 1085-1102 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	adaptive metropolis–hastings (am-h) algorithm frequency distribution markov chain monte carlo (mcmc) parameter optimization parameter uncertainty analysis rainfall frequency modeling Environmental technology. Sanitary engineering TD1-1066
spellingShingle	adaptive metropolis–hastings (am-h) algorithm frequency distribution markov chain monte carlo (mcmc) parameter optimization parameter uncertainty analysis rainfall frequency modeling Environmental technology. Sanitary engineering TD1-1066 Xingpo Liu Chengfei Xia Yifan Tang Jiayang Tu Huimin Wang Parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis–Hastings algorithm
description	A new parameter optimization and uncertainty assessment procedure using the Bayesian inference with an adaptive Metropolis–Hastings (AM-H) algorithm is presented for extreme rainfall frequency modeling. An efficient Markov chain Monte Carlo sampler is adopted to explore the posterior distribution of parameters and calculate their uncertainty intervals associated with the magnitude of estimated rainfall depth quantiles. Also, the efficiency of AM-H and conventional maximum likelihood estimation (MLE) in parameter estimation and uncertainty quantification are compared. And the procedure was implemented and discussed for the case of Chaohu city, China. Results of our work reveal that: (i) the adaptive Bayesian method, especially for return level associated to large return period, shows better estimated effect when compared with MLE; it should be noted that the implementation of MLE often produces overy optimistic results in the case of Chaohu city; (ii) AM-H algorithm is more reliable than MLE in terms of uncertainty quantification, and yields relatively narrow credible intervals for the quantile estimates to be instrumental in risk assessment of urban storm drainage planning.
format	article
author	Xingpo Liu Chengfei Xia Yifan Tang Jiayang Tu Huimin Wang
author_facet	Xingpo Liu Chengfei Xia Yifan Tang Jiayang Tu Huimin Wang
author_sort	Xingpo Liu
title	Parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis–Hastings algorithm
title_short	Parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis–Hastings algorithm
title_full	Parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis–Hastings algorithm
title_fullStr	Parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis–Hastings algorithm
title_full_unstemmed	Parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis–Hastings algorithm
title_sort	parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive metropolis–hastings algorithm
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/9d38a50752a74a68828bf7e3b4434d7a
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Parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis–Hastings algorithm

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