College of Engineering, University of Mosul
  • Register
  • Login
  • العربیة

Al-Rafidain Engineering Journal (AREJ)

Notice

As part of Open Journals’ initiatives, we create website for scholarly open access journals. If you are responsible for this journal and would like to know more about how to use the editorial system, please visit our website at https://ejournalplus.com or
send us an email to info@ejournalplus.com

We will contact you soon

  1. Home
  2. Volume 26, Issue 2
  3. Authors

Current Issue

By Issue

By Subject

Keyword Index

Author Index

Indexing Databases XML

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Peer Review Process

News

Non-Stationarity Identification in Flow Time Series Using Wavelets Transform Technique

    Reyan H. Al-Mustafa Kamel Ali Almohseen

Al-Rafidain Engineering Journal (AREJ), 2021, Volume 26, Issue 2, Pages 267-279
10.33899/rengj.2021.130600.1113

  • Show Article
  • Download
  • Cite
  • Statistics
  • Share

Abstract

 The current study explored the possibility of using  Discrete Wavelets Transform technique (DWT) in diagnosing the  non-stationarity in hydrologic time series, which typically masks the real characteristics of those series. This helps in diagnosing the appropriate model and using it for prediction purposes.
Basically, this manuscript divided into two phases: in the first phase, a defined stochastic linear model parameter,  i.e. (ARMA (1,1)) was developed with known parameters  1 and  of (0.8 and 0.4) respectively. The ACF and PACF analyses before and after intentionally adding some defined deterministic components (such as trend, periodicity, etc.) confirm the capability of (DWT)  in diagnosing those non-stationarity sources. While phase two makes use of (DWT) technique in diagnozing the non-statioarity in an observed flow time series of al-Khabor river, Kurdistan region-Iraq,  where 24 years of flow time series is available. After removing the source of the non-stainarity diagnozed by the proposed method in the data, a stationary model (ARMA (2,1)) has been fitted. The study indicated that the proposed model was distinguished by its capabilities to work in real time, thus, the outcomes of the model is almost following the same pattern of the observed outcomes of the process under study.
Keywords:
    Non-Stationarity Discrete Wavelets Transform stochastic models Time series
Main Subjects:
  • Water resources engineering & Hydrology
  • PDF (1612 K)
  • XML
(2021). Non-Stationarity Identification in Flow Time Series Using Wavelets Transform Technique. Al-Rafidain Engineering Journal (AREJ), 26(2), 267-279. doi: 10.33899/rengj.2021.130600.1113
Reyan H. Al-Mustafa; Kamel Ali Almohseen. "Non-Stationarity Identification in Flow Time Series Using Wavelets Transform Technique". Al-Rafidain Engineering Journal (AREJ), 26, 2, 2021, 267-279. doi: 10.33899/rengj.2021.130600.1113
(2021). 'Non-Stationarity Identification in Flow Time Series Using Wavelets Transform Technique', Al-Rafidain Engineering Journal (AREJ), 26(2), pp. 267-279. doi: 10.33899/rengj.2021.130600.1113
Non-Stationarity Identification in Flow Time Series Using Wavelets Transform Technique. Al-Rafidain Engineering Journal (AREJ), 2021; 26(2): 267-279. doi: 10.33899/rengj.2021.130600.1113
  • RIS
  • EndNote
  • BibTeX
  • APA
  • MLA
  • Harvard
  • Vancouver
  • Article View: 68
  • PDF Download: 81
  • LinkedIn
  • Twitter
  • Facebook
  • Google
  • Telegram
  • Home
  • Glossary
  • News
  • Aims and Scope
  • Privacy Policy
  • Sitemap
This journal is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0)

Powered by eJournalPlus