Browsing by Author "Khozani, Zohreh Sheikh"

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Citation - WoS: 22
Citation - Scopus: 23
An ensemble genetic programming approach to develop incipient sediment motion models in rectangular channels
(ELSEVIER, 2020) Zohreh Sheikh Khozani; Mir Jafar Sadegh Safari; Ali Danandeh Mehr; Wan Hanna Melini Wan Mohtar; Mehr, Ali Danandeh; Khozani, Zohreh Sheikh; Safari, Mir Jafar Sadegh; Wan Mohtar, Wan Hanna Melini; Sheikh Khozani, Zohreh; Mohtar, Wan Hanna Melini Wan; Danandeh Mehr, Ali
Assimilating unique features of genetic programming (GP) and gene expression programming (GEP) this study introduces a hybrid algorithm which results in promising incipient non-cohesive sediment motion models. The new models use the dimensionless input parameters including relative particle size relative deposited bed thickness channel friction factor and channel bed slope to estimate particle Froude number in rectangular channels. The models' accuracy is tested using different error measures and cross-validated through comparison with that of five empirical models available in the relevant literature. The results showed enhanced accuracy of the proposed models in comparison to the existing ones with concordance correlation coefficient of 0.92 and 0.94 for parsimonious and quasi-parsimonious ensemble GP models respectively. Such superiority is attributed to the integrated use of flow fluid sediment and channel characteristics in the modeling of incipient motion. Although the new algorithm is hybrid the proposed models are explicit and precise and thus motivating to be used in practice.
Citation - WoS: 4
Citation - Scopus: 5
Stacking ensemble-based hybrid algorithms for discharge computation in sharp-crested labyrinth weirs
(Springer Science and Business Media Deutschland GmbH, 2022) Khabat Khosravi; Mir Jafar Sadegh Safari; Zohreh Sheikh Khozani; Brian Mark Crookston; Ali Golkarian; Golkarian, Ali; Sheikh Khozani, Zohreh; Safari, Mir Jafar Sadegh; Khozani, Zohreh Sheikh; Crookston, Brian; Khosravi, Khabat
Labyrinth weirs are utilized to transport a greater discharge during floods in contrast to conventional weirs due to their increased weir crest length. Nevertheless due to the increased geometric complexity of labyrinth weirs determination of accurate discharge coefficients and accordingly head-discharge ratings are quite essential issues in practical application. Hence as a first step the present study proposes the following eight standalone algorithms: decision table (DT) Kstar least median square (LMS) M5 prime (M5P) M5 rule (M5R) pace regression (PR) random forest (RF) and sequential minimal optimization (SMO). Then applying the stacking (ST) algorithm these standalone models were hybridized to predict the discharge coefficient (Cd) for sharp-crested labyrinth weirs. Potential/effective variables were constructed in the form of several independent dimensionless parameters (i.e. θ h/W L/B L/h Froude number (Fr) B/W and L/W) to predict Cd as an output. The accuracy of the developed models was examined in terms of different statistical visually based and quantitative-based error measurement criteria. The results illustrate that h/W and B/W parameters have the highest and lowest effect on the Cd prediction respectively. According to NSE all developed algorithms provided accurate performances while ST-Kstar had the highest prediction power. © 2022 Elsevier B.V. All rights reserved.