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pro vyhledávání: '"Parveen Sihag"'
Akademický článek
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Publikováno v:
Journal of Irrigation and Drainage Engineering. 147
Autor:
Diksha Puri, Parveen Sihag, Mohindra Singh Thakur, Mohammed Jameel, Aaron Anil Chadee, Mohammad Azamathulla Hazi
Publikováno v:
AIMS Environmental Science, Vol 11, Iss 4, Pp 593-609 (2024)
This study is focused on the use of random forest (RF) to forecast the streamflow in the Kesinga River basin. A total of 169 data points were gathered monthly for the years 1991–2004 to create a model for streamflow prediction. The dataset was allo
Externí odkaz:
https://doaj.org/article/a693dc419f7540c38960ff3d465f014e
Estimating the effect of sand-roughened bed on hydraulic jump characteristics using heuristic models
Autor:
Rasoul Daneshfaraz, Saad Sh. Sammen, Reza Norouzi, Sani I. Abba, Ali Salem, Reza Mirzaee, Parveen Sihag, Ahmed Elbeltagi
Publikováno v:
Results in Engineering, Vol 23, Iss , Pp 102724- (2024)
The hydraulic jump occurs in open channels and downstream of hydraulic structures because these features change the flow regime from super-critical to sub-critical. To prevent downstream channel erosion and guarantee the construction of a hydraulic j
Externí odkaz:
https://doaj.org/article/d2e0d70fdd464190a8ce58c55c31d8d7
Autor:
Ahmed Y. Mohammed, Parveen Sihag
Publikováno v:
Journal of Hydroinformatics, Vol 26, Iss 3, Pp 626-640 (2024)
This study uses machine learning (ML) to predict the end-depth structure's discharge and critical depth (yc). Linear regression, M5P, random forest, random tree, reduced error pruning tree, and Gaussian process (GP) are the ML methods used in this in
Externí odkaz:
https://doaj.org/article/643b2be33fde46ceb202e498e4537532
Autor:
Isa Ebtehaj, Hossein Bonakdari
Publikováno v:
Journal of Irrigation and Drainage Engineering. 147
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
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Autor:
Diksha Puri, Raj Kumar, Parveen Sihag, Mohindra Singh Thakur, Kahkashan Perveen, Faisal M. Alfaisal, Daeho Lee
Publikováno v:
ACS Omega, Vol 8, Iss 35, Pp 31811-31825 (2023)
Externí odkaz:
https://doaj.org/article/4b4d5698811347c4ade52c27a5b80064
Publikováno v:
Journal of Hydroinformatics, Vol 25, Iss 3, Pp 1084-1102 (2023)
This paper explores the ability of multivariate adaptive regression splines, decision trees, Gaussian processes, and multiple non-linear regression equation approaches to predict the aeration efficiency at various weirs and discusses their results. I
Externí odkaz:
https://doaj.org/article/a326c7088bd04c88991c69cb228209d7
Autor:
Ahmad Alyaseen, Arunava Poddar, Navsal Kumar, Kamel Haydar, Azhar Khan, Parveen Sihag, Daeho Lee, Raj Kumar, Tej Singh
Publikováno v:
Case Studies in Construction Materials, Vol 19, Iss , Pp e02638- (2023)
Influence of Bacillus Subtilis (B. Subtilis) bacteria on the strength characteristics of 100% recycled concrete aggregate (RCA), incorporating silica fume (SF) as a substitution of cement, has been investigated in this study. Two groups of mixes (A f
Externí odkaz:
https://doaj.org/article/646be586f39941558cc7713cd36da368