Forecast of Monthly Rainfall Time Series on the Catumbela Watershed With (S)ARIMAX Models

Arlon André, Vivaldo Palanga
{"title":"Forecast of Monthly Rainfall Time Series on the Catumbela Watershed With (S)ARIMAX Models","authors":"Arlon André, Vivaldo Palanga","doi":"10.15341/mese(2333-2581)/10.08.2022/003","DOIUrl":null,"url":null,"abstract":"Abstract: Time series data often brings about the monitoring of hydrological processes. Most hydrological data are within time, connecting their analysis indirectly with the time component. When analysing time series, it is crucial to consider the fact that it consists of an internal structure (e.g., autocorrelation, trend, or seasonal variation) where data points are considered over time, therefore, forecasting hydrological data is a crucial step regarding the performance of environmental models, engineering, and research applications, and thus, it presents a significant challenge. Due to the substandard quality of precipitation data, poor results are attained then to amend it, accurate planning and management of water resources should be achieved by relying on the presence of accurately consistent precipitation data in meteorology stations. This paper aims to give a brief overview and find optimal parameters to build a Seasonal Autoregressive Integrated Moving Average (SARIMA) model using the grid search method, diagnosing time series prediction, validating the predictive rainfall, and performing rainfall forecast for the Biópio hydrological stations in the Catumbela River till 1969.","PeriodicalId":424774,"journal":{"name":"Modern Environmental Science and Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Modern Environmental Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15341/mese(2333-2581)/10.08.2022/003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract: Time series data often brings about the monitoring of hydrological processes. Most hydrological data are within time, connecting their analysis indirectly with the time component. When analysing time series, it is crucial to consider the fact that it consists of an internal structure (e.g., autocorrelation, trend, or seasonal variation) where data points are considered over time, therefore, forecasting hydrological data is a crucial step regarding the performance of environmental models, engineering, and research applications, and thus, it presents a significant challenge. Due to the substandard quality of precipitation data, poor results are attained then to amend it, accurate planning and management of water resources should be achieved by relying on the presence of accurately consistent precipitation data in meteorology stations. This paper aims to give a brief overview and find optimal parameters to build a Seasonal Autoregressive Integrated Moving Average (SARIMA) model using the grid search method, diagnosing time series prediction, validating the predictive rainfall, and performing rainfall forecast for the Biópio hydrological stations in the Catumbela River till 1969.
用(S)ARIMAX模式预测Catumbela流域月降水时间序列
摘要:时间序列数据常常带来对水文过程的监测。大多数水文数据都是在时间范围内的,它们的分析与时间成分间接地联系在一起。在分析时间序列时,重要的是要考虑到它由一个内部结构(例如,自相关、趋势或季节变化)组成的事实,其中数据点随着时间的推移而被考虑,因此,预测水文数据是关于环境模型、工程和研究应用性能的关键步骤,因此,它提出了一个重大挑战。由于降水资料质量不达标,对其进行修正的效果较差,需要依靠气象站准确一致的降水资料来实现对水资源的准确规划和管理。本文旨在简要概述和寻找最优参数,利用网格搜索方法建立季节自回归综合移动平均(SARIMA)模型,诊断时间序列预测,验证预测降雨量,并对1969年Biópio河水文站进行降雨预报。
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 求助全文
来源期刊
CiteScore
0.30
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信