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akaturk Akademik ölçüm

Makale detayı · 2020

Evaluation of Trends and Dominant Modes in Maximum Flows in Turkey Using Discrete and Additive Wavelet Transforms

Dergi

JOURNAL OF HYDROLOGIC ENGINEERING

ISSN 1084-0699

ISSN kaydı başka bir dergiye işaret ediyor; ad YÖKSİS kaydından.

YÖKSİS OpenAlex SJR Q2 JCR Q3 Atıf 9 Yüzdelik 48.1% FWCI 0.24
Yıl
2020
Tür
article

Veri kaynağı ayrımı

  • YÖKSİS YÖKSİS makale kaydı
  • YÖKSİS dergi adı JOURNAL OF HYDROLOGIC ENGINEERING
  • Katalog eşleşmesi (ISSN) Journal of Hydrologic Engineering - ASCE
  • OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)

Özet

İngilizce (OpenAlex)

This paper aims to define trends and dominant modes in annual instantaneous maximum flows (AIMF) covering the period 1961–2014 from 10 gauge stations located in different river basins in Turkey. To achieve this aim, discrete wavelet transform (DWT) and additive wavelet transform (AWT) in conjunction with the Mann-Kendall (MK) test are used and compared for the first time. Moreover, global wavelet spectrum (GWS) is employed to test the significance of the most effective periodic components. The sequential MK test is also used to determine the start or change points of trend in AIMF series. From the MK trend results, five stations showed a statistically significant (at a 5% level) negative trend for AIMF series and short-term periodic components (2 and 4 years) were found to be the most effective components, which are responsible for producing a real trend founded on the data series. The GWS analysis indicated that the most dominant components identified are significant. In addition, the MK-z values of the most effective periods derived from AWT generally showed a better agreement with MK-z value of original time series with higher correlation coefficient compared to those of DWT. The sequential MK graphs of the AWT-based time series also produced a better harmony with the sequential MK of the original data. Finally, the results showed AWT coupled with the MK provided a very efficient and accurate analysis for defining the most effective modes in the AIMF series and can be successfully used in any hydrological time series.

Konular

  • Hydrological Forecasting Using AI
  • Energy Load and Power Forecasting
  • Hydrology and Drought Analysis

Birincil konu Hydrological Forecasting Using AI

Yazarlar

  1. MUHAMMET YILMAZ ERZURUM TEKNİK ÜNİVERSİTESİ
  2. FATİH TOSUNOĞLU ERZURUM TEKNİK ÜNİVERSİTESİ
  3. NUR HÜSEYİN KAPLAN