ROBUST EXTREME RETURN LEVEL WITH POWER NORMALIZATION FOR EXTREME EVENTS: APPLICATION OF REAL HYDROLOGY DATA

IF 0.2 Q4 MULTIDISCIPLINARY SCIENCES Suranaree Journal of Science and Technology Pub Date : 2023-10-09 DOI:10.55766/sujst-2023-04-e01485
Abdellah Belhajjam, Belbachir Mohammadine, Saad Elouardirhi
{"title":"ROBUST EXTREME RETURN LEVEL WITH POWER NORMALIZATION FOR EXTREME EVENTS: APPLICATION OF REAL HYDROLOGY DATA","authors":"Abdellah Belhajjam, Belbachir Mohammadine, Saad Elouardirhi","doi":"10.55766/sujst-2023-04-e01485","DOIUrl":null,"url":null,"abstract":"In statistical studies of rare and catastrophic phenomena the distribution of generalized extreme values under linear normalization is always chosen as the appropriate model. It used to estimate the probabilities of events that have not yet been observed. Recently, the extreme value theory (EVT) received a lot of attention both theoretically and practically using just the classical linear model (L-Model) or linear normalization of the maximum to estimate return level. So, in this paper we propose a new multiplicative model based on the distribution of generalized extreme values under non-linear normalization, whose purpose is to raise the strong and weak points between these two models. Our main goal is to use our multiplicative model (P-model) to calculate the return level, as well as the associated confidence interval. The diagnostic fit, test and statistical inference to compare the two models (linear and non-linear) are studied. Finally, a data analysis and discussion are applied at first on real hydrological data for Morocco and South of Australia, then on water levels of lake Erié in Canada. The results show that our multiplicative (non-linear) model is more adaptive because it takes into account the variation of the return period.","PeriodicalId":43478,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.2000,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Suranaree Journal of Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55766/sujst-2023-04-e01485","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

In statistical studies of rare and catastrophic phenomena the distribution of generalized extreme values under linear normalization is always chosen as the appropriate model. It used to estimate the probabilities of events that have not yet been observed. Recently, the extreme value theory (EVT) received a lot of attention both theoretically and practically using just the classical linear model (L-Model) or linear normalization of the maximum to estimate return level. So, in this paper we propose a new multiplicative model based on the distribution of generalized extreme values under non-linear normalization, whose purpose is to raise the strong and weak points between these two models. Our main goal is to use our multiplicative model (P-model) to calculate the return level, as well as the associated confidence interval. The diagnostic fit, test and statistical inference to compare the two models (linear and non-linear) are studied. Finally, a data analysis and discussion are applied at first on real hydrological data for Morocco and South of Australia, then on water levels of lake Erié in Canada. The results show that our multiplicative (non-linear) model is more adaptive because it takes into account the variation of the return period.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
极端事件的功率归一化鲁棒极端回归水平:实际水文数据的应用
在罕见和灾难性现象的统计研究中,通常选择线性归一化下的广义极值分布作为合适的模型。它用来估计尚未观察到的事件的概率。近年来,极值理论(EVT)在理论和实践上都受到了广泛的关注,它仅使用经典的线性模型(L-Model)或极值的线性归一化来估计收益水平。因此,本文提出了一种新的基于非线性归一化下广义极值分布的乘法模型,其目的是提出两种模型之间的优缺点。我们的主要目标是使用我们的乘法模型(p模型)来计算回报水平,以及相关的置信区间。研究了两种模型(线性和非线性)的诊断拟合、检验和统计推断。最后,首先对摩洛哥和澳大利亚南部的实际水文数据进行了数据分析和讨论,然后对加拿大伊利湖的水位进行了分析和讨论。结果表明,我们的乘法(非线性)模型考虑了回归期的变化,具有较好的自适应性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Suranaree Journal of Science and Technology
Suranaree Journal of Science and Technology MULTIDISCIPLINARY SCIENCES-
CiteScore
0.30
自引率
50.00%
发文量
0
期刊最新文献
REDUCTION IN GREENHOUSE GAS EMISSIONS FROM COCONUT MILK PRODUCTION PLANTS IN THAILAND ADSORPTION OF DIBENZOXAZEPINE GAS ON TRANSITION METAL-DOPED SILICON CARBIDE NANOTUBES: A THEORETICAL INVESTIGATION PRELIMINARY DEVELOPMENT OF NURSES’ PRACTICE OF PEACEFUL END-OF-LIFE CARE INSTRUMENT (NP-PECI) PREVALENCE OF SICKLE CELL ANEMIA IN YOUTH BY COST EFFECTIVE STRATEGY ALONG WITH HPLC ADAPTIVE TRAFFIC SYSTEM CONTROLLERS IN TRAFFIC ENGINEERING : A SURVEY
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1