A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Nonlinear and periodic dynamics of chaotic hydro-thermal process of Skokomish river
Tekijät: Ruskeepää Heikki, Ferreira Leonardo Nascimento, Ghorbani Mohammad Ali, Kahya Ercan, Golmohammadi Golmar, Karimi Vahid
Kustantaja: SPRINGER
Julkaisuvuosi: 2023
Lehti:: Stochastic Environmental Research and Risk Assessment
Tietokannassa oleva lehden nimi: STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
Lehden akronyymi: STOCH ENV RES RISK A
Vuosikerta: 37
Aloitussivu: 2739
Lopetussivu: 2756
Sivujen määrä: 18
ISSN: 1436-3240
eISSN: 1436-3259
DOI: https://doi.org/10.1007/s00477-023-02416-1
Verkko-osoite: https://link.springer.com/article/10.1007/s00477-023-02416-1
Tiivistelmä
This paper investigates the dynamics of the time-series of water temperature of the Skokomish River (2019-2020) at hourly time scale by employing well-known nonlinear methods of chaotic data analysis including average mutual information, false nearest neighbors, correlation exponent, and local divergence rates. The delay time and the embedding dimension were calculated as 1400 and 9, respectively. The results indicated that the thermal regime in this river is chaotic due to the correlation dimension (1.38) and the positive largest Lyapunov exponent (0.045). Furthermore, complex networks have been applied to study the periodicity of thermal time-series throughout a year. A special algorithm is then used to find the so-called communities of the nodes. The algorithm found three communities which have been called Cold, Intermediate, and Warm. The temperatures in these three communities are, respectively, in the intervals (0.8, 5.8), (5.8, 11.63), and (11.63, 15.8). This analysis indicates that highest variations in water temperature occur between warm and cold seasons, and complex networks are highly capable to analyze hydrothermal fluctuations and classify their time-series.
This paper investigates the dynamics of the time-series of water temperature of the Skokomish River (2019-2020) at hourly time scale by employing well-known nonlinear methods of chaotic data analysis including average mutual information, false nearest neighbors, correlation exponent, and local divergence rates. The delay time and the embedding dimension were calculated as 1400 and 9, respectively. The results indicated that the thermal regime in this river is chaotic due to the correlation dimension (1.38) and the positive largest Lyapunov exponent (0.045). Furthermore, complex networks have been applied to study the periodicity of thermal time-series throughout a year. A special algorithm is then used to find the so-called communities of the nodes. The algorithm found three communities which have been called Cold, Intermediate, and Warm. The temperatures in these three communities are, respectively, in the intervals (0.8, 5.8), (5.8, 11.63), and (11.63, 15.8). This analysis indicates that highest variations in water temperature occur between warm and cold seasons, and complex networks are highly capable to analyze hydrothermal fluctuations and classify their time-series.
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