Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Abdulreza Karbassi"'
Publikováno v:
Rudarsko-geološko-naftni Zbornik, Vol 33, Iss 1, Pp 15-22 (2018)
Due to the time-consuming procedure for determining the 5-day biochemical oxygen demand (BOD5), the present study developed two software sensors based on artificial intelligence techniques to estimate this indicator instantaneously. For this purpose,
Externí odkaz:
https://doaj.org/article/4a24430f7b9d45b28814038a7dfd2e0b
Publikováno v:
آب و فاضلاب, Vol 21, Iss 4, Pp 104-109 (2011)
The main objective of the present work is to predict the longitudinal dispersion coefficient in natural streams using a neural network (NN) model which was developed based on Quasi-Newton training functions. For this reason, we used the hydraulic and
Externí odkaz:
https://doaj.org/article/9850699bb74247b2814f233ebb50c3e6
Publikováno v:
Rudarsko-geološko-naftni Zbornik, Vol 33, Iss 1, Pp 15-22 (2018)
Due to the time-consuming procedure for determining the 5-day biochemical oxygen demand (BOD5), the present study developed two software sensors based on artificial intelligence techniques to estimate this indicator instantaneously. For this purpose,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93cfd7fc1b13bf4d1667b8fbdd2afa83
https://doi.org/10.17794/rgn.2018.1.3
https://doi.org/10.17794/rgn.2018.1.3
Autor:
Hund-Der Yeh, Khosro Ashrafi, Saber Moazami, Abdulreza Karbassi, S. M. Bateni, Roohollah Noori, Najmeh Rezazadeh, Fatemeh Torabi Kachoosangi
Publikováno v:
Journal of Hydrology. 530:645-656
Summary When the number of computational grids increases, water quality simulation complexities arise. Therefore, using a reduced order framework to express the variations of the objective parameter may facilitate the simulation task and also the int
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d62289c85558fac79d50dc782b9ca8e
https://doi.org/10.1016/j.jhydrol.2015.10.022
https://doi.org/10.1016/j.jhydrol.2015.10.022
Publikováno v:
Environmental Monitoring and Assessment. 186:4289-4297
In the present investigation, bulk and chemical partitioning of elements (Cu, Mn, Ni, Zn, Fe, Ca) together with organic matter as a loss in ignition in the Qarechay River bed sediments have been studied. The concentration of metals in Qarechay River
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ab9c4e3e9bc40cb1db814163236adef
https://doi.org/10.1007/s10661-014-3698-5
https://doi.org/10.1007/s10661-014-3698-5
Publikováno v:
Journal of Coastal Research. 289:847-854
Shamkhali Chenar, S.; Karbassi, A.; Hajizadeh Zaker, N., and Ghazban, F., 2013. Electroflocculation of metals during estuarine mixing (Caspian Sea). Estuaries as a geochemical reactor can change the chemical forms of trace metals during the mixing of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d39118075905c93d223478e42e1cde2c
https://doi.org/10.2112/jcoastres-d-11-00224.1
https://doi.org/10.2112/jcoastres-d-11-00224.1
Autor:
Aida Biati, Abdulreza Karbassi
Publikováno v:
Environmental Monitoring and Assessment. 184:6903-6911
Flocculation of dissolved Cu, Mn, Ni, Zn, and Pb during mixing of Siyahrud River water with water sample of Caspian Sea at nine different salinity regimes was investigated. The maximum flocculation of elements occurs in the salinities 1.67‰ to 3.67
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1427b2d75ff0a2fe34764c3c398ae99
https://doi.org/10.1007/s10661-011-2466-z
https://doi.org/10.1007/s10661-011-2466-z
Autor:
Amir Khakpour, Mohammadreza Vesali-Naseh, Abdulreza Karbassi, Mohammadreza Shahbazbegian, Roohollah Noori, Hassan Mohammadi Khalf Badam
Publikováno v:
Environmental Modeling & Assessment. 17:411-420
Canonical correlation analysis (CCA), principal component analysis (PCA), and principal factor analysis (PFA) have been adopted to provide ease of understanding: interpretation of a large complex data set in the Gorganrud River monitoring networks, e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10129ca65ef819e93498cb0380aa65ee
https://doi.org/10.1007/s10666-011-9302-2
https://doi.org/10.1007/s10666-011-9302-2
Publikováno v:
Journal of Environmental Management. 91:767-771
Artificial neural networks (ANNs) are suitable for modeling solid waste generation. In the present study, four training functions, including resilient backpropagation (RP), scale conjugate gradient (SCG), one step secant (OSS), and Levenberg-Marquard
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8220d437fb611be12a0cef3204433672
https://doi.org/10.1016/j.jenvman.2009.10.007
https://doi.org/10.1016/j.jenvman.2009.10.007
Publikováno v:
Environmental Engineering Science. 26:1503-1510
Much research is carried out for predicting the longitudinal dispersion coefficient (LDC) in natural streams based on regression models. However, few methods are accurate enough to predict the LDC parameter satisfactorily. In the present investigatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d197e5836f0e7bafe012c270f8eb490
https://doi.org/10.1089/ees.2008.0360
https://doi.org/10.1089/ees.2008.0360