气候变化领域集成服务门户(CCPortal): MODELING EXTREMAL STREAMFLOW USING DEEP LEARNING APPROXIMATIONS AND A FLEXIBLE SPATIAL PROCESS

CCPortal

DOI	10.1214/23-AOAS1847
	MODELING EXTREMAL STREAMFLOW USING DEEP LEARNING APPROXIMATIONS AND A FLEXIBLE SPATIAL PROCESS
	Majumder, Reetam; Reich, Brian J.; Shaby, Benjamin A.
发表日期	2024
ISSN	1932-6157
EISSN	1941-7330
起始页码	18
结束页码	2
卷号	18 期号:2
英文摘要	Quantifying changes in the probability and magnitude of extreme flooding events is key to mitigating their impacts. While hydrodynamic data are inherently spatially dependent, traditional spatial models, such as Gaussian processes, are poorly suited for modeling extreme events. Spatial extreme value models with more realistic tail dependence characteristics are under active development. They are theoretically justified but give intractable likelihoods, making computation challenging for small datasets and prohibitive for continental-scale studies. We propose a process mixture model (PMM) which specifies spatial dependence in extreme values as a convex combination of a Gaussian process and a max-stable process, yielding desirable tail dependence properties but intractable likelihoods. To address this, we employ a unique computational strategy where a feed-forward neural network is embedded in a density regression model to approximate the conditional distribution at one spatial location, given a set of neighbors. We then use this univariate density function to approximate the joint likelihood for all locations by way of a Vecchia approximation. The PMM is used to analyze changes in annual maximum streamflow within the U.S. over the last 50 years and is able to detect areas which show increases in extreme streamflow over time.
英文关键词	Gaussian process; max-stable process; neural networks; spatial extremes; Vecchia ap- proximation
语种	英语
WOS研究方向	Mathematics
WOS类目	Statistics & Probability
WOS记录号	WOS:001202404100003
来源期刊	ANNALS OF APPLIED STATISTICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/296228
作者单位	North Carolina State University; North Carolina State University; Colorado State University
推荐引用方式 GB/T 7714	Majumder, Reetam,Reich, Brian J.,Shaby, Benjamin A.. MODELING EXTREMAL STREAMFLOW USING DEEP LEARNING APPROXIMATIONS AND A FLEXIBLE SPATIAL PROCESS[J],2024,18(2).
APA	Majumder, Reetam,Reich, Brian J.,&Shaby, Benjamin A..(2024).MODELING EXTREMAL STREAMFLOW USING DEEP LEARNING APPROXIMATIONS AND A FLEXIBLE SPATIAL PROCESS.ANNALS OF APPLIED STATISTICS,18(2).
MLA	Majumder, Reetam,et al."MODELING EXTREMAL STREAMFLOW USING DEEP LEARNING APPROXIMATIONS AND A FLEXIBLE SPATIAL PROCESS".ANNALS OF APPLIED STATISTICS 18.2(2024).