Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Hamid Moeeni"'
Autor:
Isa Ebtehaj, Abdolmajid Mahoammadian, Hamid Moeeni, Mohammad Zeynoddin, Hossein Bonakdari, Bahram Gharabaghi
Publikováno v:
Theoretical and Applied Climatology. 135:1157-1177
Soil temperature (ST) is an important dynamic parameter, whose prediction is a major research topic in various fields including agriculture because ST has a critical role in hydrological processes at the soil surface. In this study, a new linear meth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e5fdc3d0f4a74df8f6eb66f06f493b1
https://doi.org/10.1007/s00704-018-2436-2
https://doi.org/10.1007/s00704-018-2436-2
Autor:
Hamid Moeeni, Hossein Bonakdari
Publikováno v:
Water Resources Management. 32:845-863
The suspended sediment load in rivers is an important parameter in watershed planning and management. Since daily suspended sediment time series contain linear and nonlinear components, existing prediction models are associated with limitations. Ther
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66383417a0d553d348046610da14c1b7
https://doi.org/10.1007/s11269-017-1842-z
https://doi.org/10.1007/s11269-017-1842-z
Publikováno v:
Water Resources Management. 31:2141-2156
Regarding the complexity and limitations of current knowledge, monthly inflow prediction is often not sufficiently accurate and cannot fulfil the needs in water resource planning. Such time series consist of periodic and random components. Thus, by u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d0a1859c32febc7b79495141c2b80c0c
https://doi.org/10.1007/s11269-017-1632-7
https://doi.org/10.1007/s11269-017-1632-7
Publikováno v:
Journal of Hydrology. 547:348-364
Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on ti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85a0ef712764abbc56d6b14667edcf83
https://doi.org/10.1016/j.jhydrol.2017.02.012
https://doi.org/10.1016/j.jhydrol.2017.02.012
Publikováno v:
INAE Letters. 2:13-23
In this study, the performance of Seasonal Autoregressive Integrated Moving Average (SARIMA) models and hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) method in forecasting the monthly inflow to a dam is examined and compared. The number
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::45ca73eefa33037d21b9f18c1dd5734b
https://doi.org/10.1007/s41403-017-0017-9
https://doi.org/10.1007/s41403-017-0017-9
Autor:
Hossein Bonakdari, Hamid Moeeni
Publikováno v:
Stochastic Environmental Research and Risk Assessment. 31:1997-2010
The optimal operation of dam reservoirs can be programmed and managed by predicting the inflow to these structures more accurately. To this end, there are various linear and nonlinear models. However, some hydrological problems like inflow with extre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e38751912393baa902edb1a87729d62
https://doi.org/10.1007/s00477-016-1273-z
https://doi.org/10.1007/s00477-016-1273-z