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Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches

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Journal of Mountain Science 2020年第2期17卷 480-491页

作者： kiyoumars roushangar Saman SHAHNAZI Department of Water Resource Engineering Faculty of Civil EngineeringUniversity of TabrizTabrizIran Center of Excellence in Hydroinformatics University of Tabriz29 Bahman AveTabrizIran

It is important to have a reasonable estimation of sediment transport rate with respect to its significant role in the planning and management of water resources projects. The complicate nature of sediment transport in gravel-bed rivers causes inaccuracies of empirical formulas in the prediction of this phenomenon. Artificial intelligences as alternative approaches can provide solutions to such complex problems. The present study aimed at investigating the capability of kernel-based approaches in predicting total sediment loads and identification of influential parameters of total sediment transport. For this purpose, Gaussian process regression(GPR), Support vector machine(SVM) and kernel extreme learning machine(KELM) are applied to enhance the prediction level of total sediment loads in 19 mountain gravel-bed streams and rivers located in the United States. Several parameters based on two scenarios are investigated and consecutive predicted results are compared with some well-known formulas. Scenario 1 considers only hydraulic characteristics and on the other side, the second scenario was formed using hydraulic and sediment properties. The obtained results reveal that using the parameters of hydraulic conditions asinputs gives a good estimation of total sediment loads. Furthermore, it was revealed that KELM method with input parameters of Froude number(Fr), ratio of average velocity(V) to shear velocity(U*) and shields number(θ) yields a correlation coefficient(R) of 0.951, a Nash-Sutcliffe efficiency(NSE) of 0.903 and root mean squared error(RMSE) of 0.021 and indicates superior results compared with other methods. Performing sensitivity analysis showed that the ratio of average velocity to shear flow velocity and the Froude number are the most effective parameters in predicting total sediment loads of gravel-bed rivers.

关键词： Total sediment loads Support vector machine Gaussian process regression Kernel extreme learning machine Mountain Rivers

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维普期刊数据库

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A multiscale spatio-temporal framework to regionalize annual precipitation using k-means and self-organizing map technique

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Journal of Mountain Science 2018年第7期15卷 1481-1497页

作者： kiyoumars roushangar Farhad ALIZADEH Department of Water Resources Engineering Faculty of Civil EngineeringUniversity of Tabriz

Determination of homogenous precipitation-based regions is a very important task in effective management of water resources. The present study tried to propose an effective precipitation-based regionalization methodology by conjugating both temporal pre-processing and spatial clustering approaches in a way to take advantage of multiscale properties of precipitation time series. Annual precipitation data of 51 years(1960-2010) for 31 rain gauges(RGs) were collected and used in proposed clustering approaches. Discreet wavelet transform(DWT) was used to capture the time-frequency attributes of the time series and multiscale regionalization was performed by using k-means and Self Organizing Maps(SOM) clustering techniques. Daubechies function(db) was selected as mother wavelet to decompose the precipitation time series. Also, proper boundary extensions and decomposition level were applied. Different combinations of the approximation(A) and detail(D) coefficients were used to determine the input dataset as a basis of spatial clustering. The proposed model's efficiency in spatial clustering stage was verified using three different indexes namely, Silhouette Coefficient(SC), Dunn index and Davis Bouldin index(DB). Results approved superior performance of k-means technique in comparison to SOM. It was also deduced that DWT-based regionalization methodology showed improvements in comparison to historical-based models. Cross mutual information was used to investigate the RGs of cluster 3's homogeneousness in DWT-k-means approach. Results of non-linear correlation approach verified homogeneity of cluster 3. Verifications based on mean annual precipitation values of rain gauges in each cluster also approved the capability of multiscale approach in precipitation regionalization.

关键词： Precipitation Discrete wavelet transform (DWT) k-means Self Organizing Map(SOM) Iran

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维普期刊数据库

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Local vs. cross station simulation of suspended sediment load in successive hydrometric stations: heuristic modeling approach

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Journal of Mountain Science 2016年第10期13卷 1773-1788页

作者： kiyoumars roushangar Shabnam HOSSEINZADEH Jalal SHIRI Department of Civil Hydraulics Engineering Faulty of Civil Engineering University of Tabriz Department of Water Engineering Faculty of Agriculture University of Tabriz

The present paper aims at modeling suspended sediment load(SSL) using heuristic data driven methodologies, e.g. Gene Expression Programming(GEP) and Support Vector Machine(SVM) in three successive hydrometric stations of Housatonic River in U.S. The simulations were carried out through local and cross-station data management scenarios to investigate the interrelations between the SSL values of upstream/downstream stations. The available scenarios were applied to predict SSL values using GEP to obtain the best models. Then, the best models were predicted by SVM approach and the obtained results were compared with those of GEP. The comparison of the results revealed that the SVM technique is more capable than the GEP for modeling the SSL through the both local and cross-station data management strategies. Besides, local application seems to be better than cross-station application for modeling SSL. Nevertheless, the cross-station application demonstrated to be a valid methodology for simulating SSL, which would be of interest for the stations with lack of observational data. Also, the prediction capability of conventional Sediment Rating Curve(SRC) method was compared with those of GEPand SVM techniques. The obtained results revealed the superiority of GEP and SVM-based models over the traditional SRC technique in the studied stations.

关键词： Suspended sediment load Successive hydrometric stations Gene expression programming Support vector machine

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维普期刊数据库

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Estimation of bedload discharge in sewer pipes with different boundary conditions using an evolutionary algorithm

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International Journal of Sediment Research 2017年第4期32卷 564-574页

作者： kiyoumars roushangar Roghayeh Ghasempour Department of Civil Engineering The University of Tabriz

Sediment transport is a complex phenomenon due to the nonlinearity and uncertainties of the *** present study applies Gene Expression Programming（GEP） to develop bedload transport models in sewer pipes. In this regard, two types of bedload were considered: loose bed（deposition state） and rigid bed（limit of deposition state）. In order to develop the models, two scenarios with different input combinations were considered: Scenario 1 considers only hydraulic characteristics and Scenario 2 considers both hydraulic and sediment characteristics as inputs for modeling bedload discharge. The results proved the capability of GEP in prediction of sediment transport and it was found that for prediction of bedload transport in sewer pipes Scenario 2 performed more successfully than Scenario 1. According to the outcome of sensitivity analysis, Frm（Modified Froude number） and d50/y（relative particle size） for rigid boundary and Frmfor loose boundary had key roles in the modeling. The outcome of the GEP models also was compared with existing empirical equations and it was found the GEP models yielded better results. It was also found that pipe diameter affected the transport capacity of the sewer pipe. Increasing pipe diameter caused an increase in model efficiency. A pipe with a diameter of 305 mm yielded to the best results.

关键词： Sediment transport Loose boundary Rigid boundary Genetic expression programming Empirical equation

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Evaluation of the parameters affecting the roughness coefficient of sewer pipes with rigid and loose boundary conditions via kernel based approaches

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International Journal of Sediment Research 2020年第2期35卷 171-179页

作者： kiyoumars roushangar Roghayeh Ghasempour Sanam Biukaghazadeh Department of Water Resource Engineering Faculty of Civil EngineeringUniversity of Tabriz Center of Excellence in Hydroinformatics University of Tabriz

One of the important issues in water transport and sewer systems is determining the flow resistance and roughness *** accurate estimation of the roughness coefficient is a substantial issue in the design and operation of hydraulic structures such as sewer pipes,the calculation of water depth and flow velocity,and the accurate characterization of energy *** current study,applies two kernel based approaches [Support Vector Machine(SVM) and Gaussian Process Regression(GPR)] to develop roughness coefficient models for sewer *** the modeling process,two types of sewer bed conditions were considered:loose bed and rigid *** order to develop the models,different input combinations were considered under three scenarios(Scenario 1:based on hydraulic characteristics,Scenarios2 and 3:based on hydraulic and sediment characteristics with and without considering sediment concentration as input).The results proved the capability of the kernel based approaches in prediction of the roughness coefficient and it was found that for prediction of this parameter in sewer pipes Scenario 3 performed better than Scenarios 1 and ***,the sensitivity analysis results showed that Dgr(Dimensionless particle number) for a rigid bed and wb/y(ratio of deposited bed width,wb,to flow depth,y) for a loose bed had the most significant impact on the modeling process.

关键词： Gaussian process regression Loose bed Rigid bed Roughness coefficient Sewer pipes Support vector machine

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Predicting characteristics of dune bedforms using PSO-LSSVM

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International Journal of Sediment Research 2017年第4期32卷 515-526页

作者： kiyoumars roushangar Seyed Mahdi Saghebian Dominique Mouaze Division of Hydraulics Department of Civil EngineeringUniversity of Tabriz Morphodynamic Laboratory University of Caen

Dunes have a large influence on hydraulic roughness, and, thereby, on water levels which could affect the navigability of rivers and performance of hydraulic structures. The present study investigated the variation of geometric and topographic characteristics of dune bedforms and flow features as measured in laboratory studies(data sets from laboratory experiments) to estimate the roughness coefficient and characteristics of dune height. The Least Squares Support Vector Machine(LSSVM), which was optimized using Particle Swarm Optimization(PSO), was used as the Meta model approach to predict the values of interest. Developed models were separated into three categories: modeling using flow characteristics,modeling of flow and bedform characteristics, and modeling by using flow and sediment *** was found that for estimation of the roughness coefficient in open channels with dune bedforms,models developed based on flow and sediment characteristics performed more successfully. The model with input parameters of flow and grain Reynolds numbers(Re and R, respectively) and the ratio of the hydraulic radius(R) to the median grain diameter(D) yields a squared correlation coefficient(R2) of0.8609, a coefficient of determination(DC) of 0.7361,and a root mean square error(RMSE) of 0.0034 for a test series of Manning’s roughness coefficient which was the most accurate model. Results proved the key role of flow Reynolds number(Re) values as an input feature for all models predicting the roughness coefficient. Accordingly, classic approaches led to poor results in comparison. On the other hand, results obtained for estimated values of relative dune height led to moderate prediction quality, which albeit,outperformed classic approaches.

关键词： Alluvial channel Hydraulic characteristics Dune Roughness coefficient Least Squares Support Vector machine Particle Swarm Optimization

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Linear and non-linear approaches to predict the Darcy-Weisbach friction factor of overland flow using the extreme learning machine approach

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International Journal of Sediment Research 2018年第4期33卷 415-432页

作者： kiyoumars roushangar Saba Mirza Alipour Dominique Mouaze Division of Hydraulics Department of Civil Engineering University of Tabriz Morphodynamic Laboratory University of Caen

In modeling of overland flow and erosion, the overland flow friction factor(f), is a crucial factor. Due to the importance of a good understanding of f and its variability, the current study aimed to investigate the capability of non-linear approaches to estimate the Darcy-Weisbach friction factor of overland flow and its components(sediment transport, wave, form, and grain friction factors) through the Extreme Learning Machine(ELM) approach. Four datasets were used herein which were obtained from flume experiments done by different researchers. In order to investigate the effects of different parameters on the friction factor, numerous models consisting of various parameters were utilized to predict the friction factor using the ELM approach. The modeling procedure was established in two stages; the first stage aimed to model the overland flow friction factor and investigate the effect of the different parameters on the friction factor using non-linear separation via the ELM approach. In the second stage, the friction factor was linearly separated into different types of friction factors and then the separate components were estimated. Sensitivity analysis results confirmed the key role of Froude number(Fr) values for most of the models. On the other hand, the results obtained for estimated values of the friction factor were acceptable and outperformed available empirical approaches.

关键词： Overland flow Extreme Learning Machine(ELM) Friction factor Sediment transport friction factor Wave friction factor Grain friction factor Form friction factor

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Influence of surface roughness of dune bedforms on flow and turbulence characteristics

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International Journal of Sediment Research 2020年第6期35卷 666-678页

作者： Veysel Sadan Ozgur Kirca Seyed Mahdi Saghebian kiyoumars roushangar Oral Yagci Istanbul Technical University Department of Civil Engineering Department of Civil Engineering Ahar Branch Islamic Azad University Division of Hydraulics Department of Civil EngineeringUniversity of Tabriz

The current paper investigates the flow and turbulence characteristics over dune bedforms by means of laboratory experiments,where spatially dense and temporally high frequency velocity measurements were *** similar studies are available in the literature,the focus and novelty of the current study is to assess the influence of surface roughness of the dune bedforms on the nearbed *** direct comparison,two different surface roughness heights over idealized,fixed-shaped,high-angled dune bedforms were tested;one with a hydraulically-smooth surface,and the other with a fully-rough *** variation of time-averaged flow as well as turbulence statistics were examined,which was complemented by streamline plots and spectral *** results are interpreted from sediment entrainment and sediment transport points of *** results show that increased dune surface roughness reduces the nearbed flow velocity,but increases the flow velocities at upper *** upward directed flow near the dune crests becomes stronger in the case of smooth surface,while the reattachment point moves further downstream compared to the rough wall *** is concluded that the roughness of the dune surface affects the nearbed flow and turbulence characteristics qualitatively and quantitatively,which is shown to have direct consequences on sediment entrainment characteristics.

关键词： Bedforms Dunes Friction drag Turbulence Grain roughness Form drag

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