Browsing by Author "Safari, M. J. S."
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Item Open Access Fast multi-output relevance vector regression for joint groundwater and lake water depth modeling(Elsevier, 2022-08) Safari, M. J. S.; Rahimzadeh Arashloo, Shervin; Vaheddoost, B.Fast multi-output relevance vector regression (FMRVR) algorithm is developed for simultaneous estimation of groundwater and lake water depth for the first time in this study. The FMRVR is a multi-output regression analysis technique which can simultaneously predict multiple outputs for a multi-dimensional input. The data used in this study is collected from 34 stations located in the lake Urmia basin over a 40-year time period. The performance of the FMRVR model is examined in contrast to the support vector regression (SVR) and multi-linear regression (MLR) benchmarks. Results reveal that FMRVR is able to generate more accurate estimation for groundwater and lake water depth with coefficient of determination (R2) of 0.856 and 0.992 and root mean square error (RMSE) of 0.857 and 0.083, respectively. The outperformance of FMRVR can be linked to its capability for a joint estimation of multiple relevant outputs by taking into account possible correlations among the outputs.Item Open Access Kernel ridge regression model for sediment transport in open channel flow(Springer, 2021-01-11) Safari, M. J. S.; Arashloo, Shervin RahimzadehSediment transport modeling is of primary importance for the determination of channel design velocity in lined channels. This study proposes to model sediment transport in open channel flow using kernel ridge regression (KRR), a nonlinear regression technique formulated in the reproducing kernel Hilbert space. While the naïve kernel regression approach provides high flexibility for modeling purposes, the regularized variant is equipped with an additional mechanism for better generalization capability. In order to better tailor the KRR approach to the sediment transport modeling problem, unlike the conventional KRR approach, in this study the kernel parameter is directly learned from the data via a new gradient descent-based learning mechanism. Moreover, for model construction, a procedure based on Cholesky decomposition and forward-back substitution is applied to improve the computational complexity of the approach. Evaluation of the recommended technique is performed utilizing a large number of laboratory experimental data where the examination of the proposed approach in terms of three statistical performance indices for sediment transport modeling indicates a better performance for the developed model in particle Froude number computation, outperforming the conventional models as well as some other machine learning techniques.Item Open Access Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm(Elsevier, 2020) Safari, M. J. S.; Arashloo, Shervin Rahimzadeh; Mehr, A. D.In this study, Regression in the Reproducing Kernel Hilbert Space (RRKHS) technique which is a non-linear regression approach formulated in the reproducing kernel Hilbert space (RRKHS) is applied for rainfall-runoff (R-R) modeling for the first time. The RRKHS approach is commonly applied when the data to be modeled is highly non-linear, and consequently, the common linear approaches fail to provide satisfactory performance. The calibration and verification processes of the RRKHS for one- and multi-day ahead forecasting R-R models were demonstrated using daily rainfall and streamflow measurement from a mountainous catchment located in the Black Sea region, Turkey. The efficacy of the new approach in each forecasting scenario was compared with those of other benchmarks, namely radial basis function artificial neural network and multivariate adaptive regression splines. The results illustrate the superiority of the RRKHS approach to its counterparts in terms of different performance indices. The range of relative peak error (PE) is found as 0.009–0.299 for the best scenario of the RRKHS model, which illustrates the high accuracy of RRKHS in peak streamflow estimation. The superior performance of the RRKHS model may be attributed to its formulation in a very high (possibly infinite) dimensional space which facilitates a more accurate regression analysis. Based on the promising results of the current study, it is expected that the proposed approach would be applied to other similar environmental modeling problems.Item Open Access Robust low-rank learning multi-output regression for incipient sediment motion in sewer pipes(Elsevier BV, 2023-08-30) Safari, M. J. S.; Rahimzadeh Arashloo, ShervinThe existing incipient sediment motion models typically apply conventional regression methods considering either velocity or shear stress. In the current study, incipient sediment motion is analyzed through a simultaneous and joint analysis of velocity and shear stress using the robust low-rank learning (RLRL) multi-output regression technique. Moreover, the experimental data compiled from five different channels are utilized to develop a generic incipient sediment motion model valid for a channel of any cross-sectional shape. The efficiency of the developed method is examined and compared against the available conventional regression models. The experimental results indicate that the RLRL model yields better results than its counterparts. In particular, while cross-section specific models fail to provide accurate estimates for shear stress or velocity for other cross sections, the proposed model provides satisfactory results for all channel shapes. The better performance of the recommended approach can be attributed to the joint modeling of the shear stress and the velocity which is realized by capturing the correlation between these parameters in terms of a low rank output mixing matrix which enhances the prediction performance of the approach.