Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Hadi Galavi"'
Autor:
Nariman Valizadeh, Ahmed El-Shafie, Majid Mirzaei, Hadi Galavi, Muhammad Mukhlisin, Othman Jaafar
Publikováno v:
The Scientific World Journal, Vol 2014 (2014)
Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydr
Externí odkaz:
https://doaj.org/article/eefde64033604edebb12ce10f23ad818
Publikováno v:
Stochastic Environmental Research and Risk Assessment. 37:1213-1227
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3db3a72bb42676e174f32d43368afd99
https://doi.org/10.1007/s00477-022-02337-5
https://doi.org/10.1007/s00477-022-02337-5
Publikováno v:
Sustainable Water Resources Management. 9
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dabfe43899fd11f2ed64463bc7d5ff59
https://doi.org/10.1007/s40899-023-00839-w
https://doi.org/10.1007/s40899-023-00839-w
Publikováno v:
Stochastic Environmental Research and Risk Assessment. 36:4255-4269
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52ca54d6a3e5e770b1c56a526013ca71
https://doi.org/10.1007/s00477-022-02261-8
https://doi.org/10.1007/s00477-022-02261-8
Publikováno v:
Processes; Volume 10; Issue 11; Pages: 2261
Advanced manufacturing technology (AMT) has the potential to significantly improve manufacturing performance and boost competitiveness in the global market. Investment in AMT remains a promising but potentially risky venture due to the numerous facto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07ebbe59a9a3a2d0fd67357b1d7e27fc
Autor:
Hadi Galavi, Majid Mirzaei
Publikováno v:
Water Resources Management. 34:2097-2109
Uncertainty in climate change impact studies can be identified in general circulation models (GCM), emission scenarios, downscaling techniques, hydrological models and data. Despite being aleatory or epistemic -sourced uncertainties, their effect siz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c1fec0fe3309ee7d8973f559a28e49e
https://doi.org/10.1007/s11269-020-02553-0
https://doi.org/10.1007/s11269-020-02553-0
Publikováno v:
Soil and Tillage Research. 226:105579
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::67d7fbba2f8533a2e7197f21da542535
https://doi.org/10.1016/j.still.2022.105579
https://doi.org/10.1016/j.still.2022.105579
Publikováno v:
Theoretical and Applied Climatology. 137:1289-1303
Hydrological models are commonly used to quantify the hydrological impacts of climate change using general circulation model (GCM) simulations as input. However, application of the model results with respect to future changes in streamflow scenarios
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89617672264c398f12c1b5b10313c843
https://doi.org/10.1007/s00704-018-2669-0
https://doi.org/10.1007/s00704-018-2669-0
Autor:
Mohd Amin Mohd Soom, Ahmad Ainuddin Nuruddin, Hadi Galavi, Liew Ju Neng, Mahboubeh Ebrahimian, Alias Mohd Sood
Publikováno v:
Singapore Journal of Tropical Geography. 39:192-214
Trend analyses of monthly, seasonal and annual rainfall, air temperature, and streamflow were performed using Mann‐Kendall test within the Langat River basin to identify gradual trends and abrupt shifts for 1980 − 2010. Annual rainfall showed an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0536e19dd4dc84a22baeec11bd124b1b
https://doi.org/10.1111/sjtg.12234
https://doi.org/10.1111/sjtg.12234
Autor:
Haoxuan Yu, Vahid Shokri, Hadi Galavi, Adnan Dehghani, Sahar Mohsenzadeh Karimi, Majid Mirzaei, Mehdi Sookhak
Publikováno v:
Sustainability, Vol 13, Iss 13384, p 13384 (2021)
Sustainability; Volume 13; Issue 23; Pages: 13384
Sustainability; Volume 13; Issue 23; Pages: 13384
Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning tec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22c93d1b42e5869b3a6cf57a2f47ffdb
https://doi.org/10.3390/su132313384
https://doi.org/10.3390/su132313384