Browsing by Author "Samadianfard, Saeed"

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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: 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.