Browsing by Author "Arashloo, Shervin Rahimzadeh"

Now showing 1 - 9 of 9

Citation - WoS: 10
Citation - Scopus: 11
Fast multi-output relevance vector regression for joint groundwater and lake water depth modeling
(Elsevier Ltd, 2022) Mir Jafar Sadegh Safari; Shervin Rahimzadeh Arashloo; Babak Vaheddoost; Rahimzadeh Arashloo, Shervin; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh
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. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Lq-norm multiple kernel fusion regression for self-cleansing sediment transport
(Springer Nature, 2024) Mir Jafar Sadegh Safari; Shervin Rahimzadeh Arashloo; Mehrnoush Kohandel Gargari; Rahimzadeh Arashloo, Shervin; Gargari, Mehrnoush Kohandel; Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh; Kohandel Gargari, Mehrnoush
Experimental and modeling studies have been conducted to develop an approach for self-cleansing rigid boundary open channel design such as drainage and sewer systems. Self-cleansing experiments in the literature are mostly performed on circular channel cross-section while a few studies considered self-cleansing sediment transport in small rectangular channels. Experiments in this study were carried out in a rectangular channel with a length of 12.5 m a width of 0.6 m a depth of 0.7 m and having an automatic control system for regulating channel slope discharge and sediment rate. Behind utilizing collected experimental data in this study existing data in the literature for rectangular channels are used to develop self-cleansing models applicable for channel design. Through the modeling procedure this study recommends Lq-norm multiple kernel fusion regression (LMKFR) techniques for self-cleansing sediment transport. The LMKFR is a regression technique based on the regularized kernel regression method which benefits from the combination of multiple information sources to improve the performance using the Lq-norm multiple kernel learning framework. The results obtained by LMKFR are compared to support vector regression benchmark and existing conventional regression self-cleansing sediment transport models in the literature for rectangular channels. The superiority of LMKFR is illustrated in an accurate modeling as compared with its alternatives in terms of various statistical error measurement criteria. The encouraging results of LMKFR can be linked to utilization of several kernels which are fused effectively using an Lq-norm prior that captures the intrinsic sparsity of the problem at hand. Promising performance of LMKFR technique in this study suggests it as an effective technique to be examined in similar environmental hydrological and hydraulic problems. © 2024 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 3
Multiple kernel fusion: A novel approach for lake water depth modeling
(Academic Press Inc., 2023) Mir Jafar Sadegh Safari; Shervin Rahimzadeh Arashloo; Babak Vaheddoost; Rahimzadeh Arashloo, Shervin; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh
Multiple kernel fusion (MKF) refers to the task of combining multiple sources of information in the Hilbert space for improved performance. Very often the combined kernel is formed as a linear composition of multiple base kernels where the combination weights are learned from the data. As the first application of an MKF approach in hydrological modeling lake water depth as one of the pivot factors in the reservoir analysis is simulated by considering different hydro-meteorological variables. The role of each individual input parameter is initially investigated by applying a kernel regression approach. We then illustrate the utility of an MKF formalism which learns kernel combination of weights to yield an optimal composition for kernel regression. A set of 40-year data collected from 27 groundwater and streamflow stations and 7 meteorological stations for precipitation and evaporation parameters in the vicinity of Lake Urmia are utilized for model development. Both visual and quantitative statistical performance criteria illustrate a superior performance for the MKF approach compared to kernel ridge regression (KRR) the support vector regression (SVR) back propagation neural network (BPNN) and auto regressive (AR) models. More specifically while each individual input parameter fails to provide an accurate prediction for lake water depth modeling an optimal combination of all input parameters incorporating the groundwater level streamflow precipitation and evaporation via a multiple kernel learning approach enhances the predictive performance of the model accuracy in the multiple scenarios. The promising results (RMSE = 0.098 m, R2 = 0.987, NSE = 0.986) may motivate the application of a MKF approach towards solving alternative and complex hydrological problems. © 2022 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
Non-Linear Output Structure Learning: A Novel Multi-Target Technique for Multi-Station and Multi-Index Drought Modelling
(WILEY, 2025) Mir Jafar Sadegh Safari; Shervin Rahimzadeh Arashloo; Babak Vaheddoost; Rahimzadeh Arashloo, Shervin; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh
Exiting artificial intelligence-based drought models estimate a single drought index in a single station. This study advances drought modelling by proposing Non-linear Output Structure Learning (NOSL) for simultaneously estimating two drought indices at eight stations. A multi-target drought model provides insights for a better understanding of the meteorological and hydrological impacts of drought. Hydro-meteorological data including precipitation evaporation and streamflow are used for a joint estimation of Streamflow Drought Index (SDI) and Standardized Precipitation Evapotranspiration Index (SPEI). The efficacy of the NOSL algorithm is examined against single-target Kernel Ridge Regression (KRR) and Fast Multi-output Relevance Vector Regression (FMRVR) models. The data during October 1981 to September 2015 at a monthly scale (408 Months) from eight different stations in Buyuk Menderes Basin (BMB) located (BMB) in Western T & uuml,rkiye are used in this study. The effects of 1- 3- and 6-month Moving Average (MA) are also considered for drought estimation. Results show that NOSL can effectively estimate the SPEI and SDI indices and outperforms KRR and FMRVR benchmarks. The effectiveness of the NOSL technique can be linked to a structural modelling mechanism based on vector-valued functions where the dependencies among output variables are captured utilising a non-linear function for enhanced performance. The developed multi-target drought model based on the NOSL technique not only helps in incorporating multiple variables in the model for a better estimation but it enhances our understanding of various aspects of droughts and building adaptive strategies and resilience map counter to drought hazard.
Citation - WoS: 40
Citation - Scopus: 44
Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm
(ELSEVIER, 2020) Mir Jafar Sadegh Safari; Shervin Rahimzadeh Arashloo; Ali Danandeh Mehr; Rahimzadeh Arashloo, Shervin; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh; Danandeh Mehr, Ali
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 mull-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.
Citation - WoS: 2
Citation - Scopus: 2
Robust low-rank learning multi-output regression for incipient sediment motion in sewer pipes
(Elsevier B.V., 2023) Mir Jafar Sadegh Safari; Shervin Rahimzadeh Arashloo; Arashloo, Shervin Rahimzadeh; Rahimzadeh Arashloo, Shervin; Safari, Mir Jafar Sadegh
The 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. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 1
Signature kernel ridge regression time series model: A novel approach for hydrological drought modeling using multi-station meteorological drought information
(ELSEVIER, 2025) Mir Jafar Sadegh Safari; Shervin Rahimzadeh Arashloo; Babak Vaheddoost; Rahimzadeh Arashloo, Shervin; Vaheddoost, Babak; Safari, Mir Jafar Sadegh; Arashloo, Shervin Rahimzadeh
In the context of growing environmental challenges and the need for sustainable water resource management hydrological drought prediction has gained prominence as a critical issue. Existing artificial intelligence and time series-based models for hydrological drought indices have traditionally been established using streamflow data. This study gives a significant progress in hydrological drought modeling through the introduction of the Signature Kernel Ridge Regression (SKRR) time series model. Instead of directly using rainfall and runoff data to develop a rainfall-runoff (RR) model the Standardized Precipitation Evapotranspiration Index (SPEI) values in neighbor meteorological stations serve as inputs for estimating the Streamflow Drought Index (SDI) in target hydrometric stations considering the 3- 6- and 12-month moving average time windows. The objective of this study is to enhance hydrological drought modeling by integrating soft computing techniques that effectively handle multivariate and irregular time series. The efficacy of the SKRR is compared with the well-established Generalized Regression Neural Network (GRNN) Random Forest (RF) and Auto Regressive Integrated Moving Average model with eXogenous input (ARIMAX). The findings indicate that SKRR is capable of precisely estimating SDI in three hydrometric stations using meteorological drought information from 14 stations outperforming the GRNN RF and ARIMAX models. The enhanced performance of the SKRR time series model stems from the utilization of a new and effective signature kernel which can be utilized for the study of irregularly sampled multivariate time series in addition to be applicable to time series of different temporal spans while being a positive-definite kernel facilitating usage in the Hilbert space. The novel drought based-RR model established by SKRR utilized various external stations' meteorological drought indices to compute the hydrological drought indices in target stations not only enhances the modeling capability but also progress our understanding of drought dynamics by showcasing the power of soft computing in handling environmental
Citation - WoS: 9
Citation - Scopus: 10
Sparse kernel regression technique for self-cleansing channel design
(Elsevier Ltd, 2021) Mir Jafar Sadegh Safari; Shervin Rahimzadeh Arashloo; Arashloo, Shervin Rahimzadeh; Rahimzadeh Arashloo, Shervin; Safari, Mir Jafar Sadegh
The application of a robust learning technique is inevitable in the development of a self-cleansing sediment transport model. This study addresses this problem and advocates the use of sparse kernel regression (SKR) technique to design a self-cleaning model. The SKR approach is a regression technique operating in the kernel space which also benefits from the desirable properties of a sparse solution. In order to develop a model applicable to a wide range of channel characteristics five different experimental data sets from 14 different channels are utilized in this study. In this context the efficacy of the SKR model is compared against the support vector regression (SVR) approach along with several other methods from the literature. According to the statistical analysis results the SKR method is found to outperform the SVR and other regression equations. In particular while empirical regression models fail to generate accurate results for other channel cross-section shapes and sizes the SKR model provides promising results due to the inclusion of a channel parameter at the core of its structure and also by operating on an extensive range of experimental data. The superior efficacy of the SKR approach is also linked to its formulation in the kernel space while also benefiting from a sparse representation method to select the most useful training samples for model construction. As such it also circumvents the requirement to evaluate irrelevant or noisy observations during the test phase of the model and thus improving on the test phase running time. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 3
Citation - Scopus: 3
Vertical and Horizontal Water Penetration Velocity Modeling in Nonhomogenous Soil Using Fast Multi-Output Relevance Vector Regression
(Mary Ann Liebert Inc., 2024) Babak Vaheddoost; Shervin Rahimzadeh Arashloo; Mir Jafar Sadegh Safari; Arashloo, Shervin Rahimzadeh; Vaheddoost, Babak; Safari, Mir Jafar Sadegh
A joint determination of horizontal and vertical movement of water through porous medium is addressed in this study through fast multi-output relevance vector regression (FMRVR). To do this an experimental data set conducted in a sand box with 300 · 300 · 150 mm dimensions made of Plexiglas is used. A random mixture of sand having size of 0.5–1 mm is used to simulate the porous medium. Within the experiments 2 3 7 and 12 cm walls are used together with different injection locations as 130.7 91.3 and 51.8 mm measured from the cutoff wall at the upstream. Then the Cartesian coordinated of the tracer time interval length of the wall in each setup and two dummy variables for determination of the initial point are considered as independent variables for joint estimation of horizontal and vertical velocity of water movement in the porous medium. Alternatively the multi-linear regression random forest and the support vector regression approaches are used to alternate the results obtained by the FMRVR method. It was concluded that the FMRVR outperforms the other models while the uncertainty in estimation of horizontal penetration is larger than the vertical one. © 2024 Elsevier B.V. All rights reserved.