Browsing by Author "Kargar, Katayoun"

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Citation - WoS: 22
Citation - Scopus: 24
Hybrid models for suspended sediment prediction: optimized random forest and multi-layer perceptron through genetic algorithm and stochastic gradient descent methods
(SPRINGER LONDON LTD, 2022) Saeed Samadianfard; Katayoun Kargar; Sadra Shadkani; Sajjad Hashemi; Akram Abbaspour; Mir Jafar Sadegh Safari; Kargar, Katayoun; Samadianfard, Saeed; Shadkani, Sadra; Safari, Mir Jafar Sadegh; Hashemi, Sajjad; Abbaspour, Akram
Owing to the nonlinear and non-stationary nature of the suspended sediment transport in rivers suspended sediment concentration (SSC) modeling is a challenging task in environmental engineering. Investigation of SSC is of paramount importance in river morphology and hydraulic structures operation. To this end for SSC modeling first random forest (RF) and multi-layer perceptron (MLP) standalone models were developed and then they were optimized with genetic algorithm (GA) and stochastic gradient descent (SGD) to develop GA-MLP GA-RF SGD-MLP and SGD-RF hybrid models. Variety of input scenarios are implemented for SSC prediction to find the best input combination. The streamflow and SSC data collected from two stations of Minnesota and San Joaquin rivers respectively located at South Dakota and California are utilized in the current study. Accuracies of the developed models are examined by means of three performance criteria of correlation coefficient (CC) scattered index (SI) and Willmott's index of agreement (WI). A significant promotion in accuracy of hybrid models has been seen in contrast to their standalone counterparts. As can be deduced from the results GA-MLP-5 and GA-RF-5 models with CC of 0.950 and 0.944 SI of 0.290 and 0.308 and WI of 0.974 and 0.971 respectively were found as best models for prediction of SSC at Minnesota river. The developed SGD-MLP-5 and SGD-RF-5 models with CC of 0.900 and 0.901 SI of 0.339 and 0.339 and WI of 0.945 and 0.946 respectively gave accurate results at San Joaquin river. Through the application of SGD algorithm the adaptive learning rate epochs rho L1 and L2 were activated and presumed as 0.004 10 1 0.000009 and 0 respectively. The ExpRectifier was considered as san activation operation due to its better efficiency in comparison with its alternatives for predicting SSC in SGD-MLP model. According to the results the fifth scenario that incorporates SSCt-1 SSCt-2 Q(t) Q(t-1) and Q(t-2) were found superior for SSC modeling in the studied rivers. The recommended hybrid algorithms based on GA and SGD optimization algorithms are proposed as practical tools for solving complex environmental problems.
Citation - WoS: 26
Citation - Scopus: 28
Invasive weed optimization-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit
(Elsevier Ltd, 2020) Mir Jafar Sadegh Safari; Babak Mohammadi; Katayoun Kargar; Kargar, Katayoun; Safari, Mir Jafar Sadegh; Mohammadi, Babak
Inasmuch as channels are designed to mitigate continues sedimentation sediment transport models have been developed to calculate flow velocity to keep sediment particles in motion. In order to promote the computation capability of sediment transport models recently machine learning algorithms have attracted interests extensively. However accuracy of such a model is attributed to the range of data and applied technique for model construction. For this purpose the current study scrutinizes the applicability of “non-deposition with deposited bed” (NDB) concept for design of large channels applying hybrid machine learning algorithms. Through the modeling firstly conventional adaptive neuro-fuzzy inference system (ANFIS) technique is applied to develop a stand-alone model. In furtherance of improving the model's performance the ANFIS is hybridized with invasive weed optimization (IWO) algorithm to construct a hybrid ANFIS-IWO model. As a benchmark the ANFIS is further hybridized with classical genetic algorithm (GA) to compare with ANFIS-IWO outcomes. Furthermore the developed machine learning models are compared to multigene genetic programming (MGP) and particle swarm optimization (PSO) stand-alone machine learning results reported in the literature and classical regression models by means of variety of statistical performance measurements. Hybridization of ANFIS with IWO enhances its accuracy with a factor of 30%. Respecting to the models performance examination the ANFIS-IWO model is found superior to its alternatives for sediment transport computation. The thickness of the deposited bed and deposited bed width are found as effective parameters for sediment transport modeling in open channels with a bed deposit. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 31
Citation - Scopus: 35
Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques
(IWA Publishing 12 Caxton Street London SW1H 0QS, 2019) Katayoun Kargar; Mir Jafar Sadegh Safari; Mirali Mohammadi; Saeed Samadianfard; Kargar, Katayoun; Samadianfard, Saeed; Safari, Mir Jafar Sadegh; Mohammadi, Mirali
Deposition of sediment is a vital economical and technical problem for design of sewers urban drainage irrigation channels and in general rigid boundary channels. In order to confine continuous sediment deposition rigid boundary channels are designed based on self-cleansing criteria. Recently instead of using a single velocity value for design of the self-cleansing channels more hydraulic parameters such as sediment fluid flow and channel characteristics are being utilized. In this study 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. The models are established based on laboratory experimental data with wide ranges of sediment and pipe sizes. The developed models’ performances have been compared with empirical equations based on two statistical factors comprising the root mean square error (RMSE) and the concordance coefficient (CC). Besides Taylor diagrams are used to test the resemblance between measured and calculated values. The outcomes disclose that NF4 as the precise NF model performs better than the best GEP model (GEP1) and regression equations. As a conclusion the obtained results proved the suitable accuracy and applicability of the NF method in Frp estimation. © 2019 Elsevier B.V. All rights reserved.
Citation - WoS: 20
Citation - Scopus: 22
Weighted instances handler wrapper and rotation forest-based hybrid algorithms for sediment transport modeling
(ELSEVIER, 2021) Katayoun Kargar; Mir Jafar Sadegh Safari; Khabat Khosravi; Kargar, Katayoun; Khosravi, Khabat; Safari, Mir Jafar Sadegh
Sediment transport modeling has been known as an essential issue and challenging task in water resources and environmental engineering. In order to minimize the adverse impacts of the continues sediment deposition that is known as a main source of pollution in the urban area the self-cleansing method is widely utilized for designing the sewer pipes to create a condition to keep the bottom of channel clean from sedimentation. In the present study an extensive data range is utilized for modeling the sediment transport in non-deposition with clean bed condition. Regarding the effective parameters involved four different scenarios are considered for the modeling. To this end four standalone methods including the M5P reduced error pruning tree (REPT) random forest (RF) and random tree (RT) and two hybrid models based on rotation forest (ROF) and weighted instances handler wrapper (WIHW) techniques are developed and result compared with three empirical equations. Based on the results the hybrid WIHW-RT and WIHW-RF models provide better performance in particle Froude number estimation in comparison to other standalone and hybrid models. Performances of the most of the models are found accurate except RT and REPT standalone models. The outcomes revealed that the empirical models have considerable overestimation. Generally hybrid data mining methods yield more precise estimations of sediment transport in contrast to the regression equations and standalone models. Particularly both WIHW-RT and WIHWRF models provide almost the same performances however as WIHW-RT can better capture the extreme particle Froude number values it slightly outperforms WIHW-RF. Promising findings of the current study may encourage the implementation of the recommended approaches in alternative hydrological problems.