Investigation of long-memory properties in streamflow time series in Gamasiab River, Iran

Hadizadeh, Reza; Eslamian, Saeid; Chinipardaz, Rahim

doi:10.1504/ijhst.2013.060335

Cited by 6 publications

(4 citation statements)

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“…29 These processes have been widely employed in recent years in the analysis of hydrological data. [30][31][32][33]12 We estimate the differencing parameter d in equation (2) using the Whittle function, which is basically an approximation to the likelihood function and use a frequency domain version of it as in Dahlhaus 34 alongside a testing procedure developed by Robinson 35 which is based on the Lagrange Multiplier (LM) principle. This method tests the null hypothesis, :…”

Section: Methodsmentioning

confidence: 99%

“…There are various studies that investigate long-memory streamflow properties for the Taiţa River (Rumania), the River Nile (Africa), and most of Canada's major hydrologic regions. [9][10][11] Since streamflow is a water volume and velocity variable, there are various methods that deal with the variability of streamflow series exhibiting longmemory, 12,11 clearly based on the early works of Hurst 1 where many time series data in diverse fields of application, including hydrology, exhibited the property of long memory or long range dependence.…”

Section: Introductionmentioning

confidence: 99%

“…The study utilizes monthly data provided by the Panama Canal Authority (ACP), between the periods of 1960 and 2020 to investigate the recent long memory dynamics and time trends present in the six main rivers of the Canal Watershed. The long-memory feature refers to a not negligible dependence between distant observations in time 12 and is a feature that is present in most of the climatological and geophysical time series. [14][15][16][17] Furthermore, the purpose of the paper is also to examine if time trends are present in the data using updated methods as proposed in Gil-Alana et al 18 and Awe and Gil-Alana 19 among many others.…”

Section: Introductionmentioning

confidence: 99%

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Long memory of river streams in the canal of Panama watershed

Luque¹,

Gil-Alana²

2023

IJH

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Study region: It refers to the time series analysis of streamflow patterns in the six principal rivers of the Canal of Panama. Study focus: Using recent time series modeling techniques in fractional or I(d) frameworks, it offer an insight into the hydrology of the Panama Canal watershed. Objectives: The main objective is to determine if the river flows at the Canal of Panama watershed display long memory, and based on that, investigate if significant trends are present across time. Novelty: The use of updated time series techniques in the analysis of long memory and time trends in river flow data. New hydrological insights for the region: The results first indicate that long memory is present in all cases, with no significant trends whatsoever. While some series display evidence of seasonality, they are all mean-reverting. The implication of the results obtained is that climatological or hydrological shocks affecting these river streamflows may take a longer time to disappear.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Long memory of river streams in the canal of Panama watershed

Luque¹,

Gil-Alana²

2023

IJH

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“…Hadizadeh et al. [14] discussed about long-memory of processes of time series at Polchehr, Polkohne and Heydarabad hydrometric stations of Gamasiab River at Kermanshah by using daily, monthly and seasonal discharge average data through heuristic, semi-parametric and parametric methods.…”

Section: Introductionmentioning

confidence: 99%

Transient responses of sandwich structure based on the generalized thermoelastic diffusion theory with memory-dependent derivative

2018

Jnl of Sandwich Structures & Materials

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The present work is devoted to investigating the transient responses of a sandwich structure based on the generalized thermoelastic diffusion theory with memory-dependent derivative. Both the left and the right bounding surfaces are subjected to a thermal shock and a chemical potential shock simultaneously. It is assumed that the values of thermal contact resistance and diffusional contact impedance at the interface are zero with ideal adhesion. The coupled governing equations containing time delay factors and kernel functions, which can be chosen freely according to specific problems, are solved by the Laplace transform together with its numerical inversion. The influences of the material characteristic parameters at the interface on the structural responses are emphatically discussed, and the non-dimensional temperature, chemical potential, displacement, stress as well as concentration at different values of time delay factors and kernel functions are obtained and illustrated graphically. The results show that: the changes of the time delay factors, kernel functions, and material characteristic parameters have the varying degrees of influences on the considered variables. Based on the above conclusions, the sandwich structure working under the thermoelastic diffusion coupling condition can be designed reasonably.

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Deep learning: Long short-term memory in hydrological time series

Lee¹,

Singh²

2023

Handbook of Hydroinformatics

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Investigation of long-memory properties in streamflow time series in Gamasiab River, Iran

Cited by 6 publications

References 0 publications

Long memory of river streams in the canal of Panama watershed

Long memory of river streams in the canal of Panama watershed

Transient responses of sandwich structure based on the generalized thermoelastic diffusion theory with memory-dependent derivative

Deep learning: Long short-term memory in hydrological time series

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