K
Katayoun Kargar
Researcher at Urmia University
Publications - 6
Citations - 408
Katayoun Kargar is an academic researcher from Urmia University. The author has contributed to research in topics: Wind speed & Multilayer perceptron. The author has an hindex of 5, co-authored 6 publications receiving 195 citations.
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Journal ArticleDOI
Predicting Standardized Streamflow index for hydrological drought using machine learning models
Shahabbodin Shamshirband,Sajjad Hashemi,Hana Salimi,Saeed Samadianfard,Esmaeil Asadi,Sadra Shadkani,Katayoun Kargar,Amir Mosavi,Narjes Nabipour,Kwok Wing Chau +9 more
TL;DR: Three indices of drought are modeled using Support Vector Regression, Gene Expression Programming, and M5 model trees and the results indicate that SPI delivered higher accuracy than SSI.
Journal ArticleDOI
Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm
Saeed Samadianfard,Sajjad Hashemi,Katayoun Kargar,Mojtaba Izadyar,Ali Mostafaeipour,Amir Mosavi,Narjes Nabipour,Shahaboddin Shamshirband +7 more
TL;DR: It was concluded that the WOA optimization algorithm could improve the prediction accuracy of the MLP model and may be recommended for accurate wind speed prediction.
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Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms
Katayoun Kargar,Saeed Samadianfard,Javad Parsa,Narjes Nabipour,Shahaboddin Shamshirband,Amir Mosavi,Kwok Wing Chau +6 more
TL;DR: In this article, the longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers, as a result of transportation processes, the concentrat...
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Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques.
TL;DR: Two techniques of neuro-fuzzy (NF) and gene expression programming (GEP) are implemented for particle Froude number (Frp) estimation of the non-deposition condition of sediment transport in rigid boundary channels, proving the suitable accuracy and applicability of the NF method in Frp estimation.
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Invasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit
TL;DR: This study scrutinizes the applicability of “non-deposition with deposited bed” (NDB) concept for design of large channels applying hybrid machine learning algorithms and finds the ANFIS-IWO model is found superior to its alternatives for sediment transport computation.