Prediction Of Sediment Concentration Research Articles

Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches

Prediction of sediment concentration in a river is very important for many water resource projects. Conventional sediment rating curves (SRC), however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. A comparison was performed between fuzzy logic (FL), SRC and multilinear regression models. It was based on a 5-year period of continuous streamflow, suspended sediment concentration and mean water temperature data of Sacremento Freeport Station operated by the United States Geological Survey. Based on the comparison of the results, it is found that the FL model gives better estimates than the other techniques.

Prediction of Sediment Concentration in Rivers by Recursive Least-Squares and Linear Minimum Variance Estimators

AbstractPeaks of suspended sediment concentration in rivers appear before peak discharge appears. The observation of discharge is usually done in many rivers for river management, but the suspended sediment concentration is not always observed. The suspended sediment concentration is expressed by a sediment-rating curve over a long period of time. Coefficients in the sediment-rating curve are time varying. The time variation of the suspended sediment concentration is predicted by the sediment-rating curve with the recursive least-squares and the linear minimum variance estimators. Because the sediment-rating curve is linearized by the logarithmic transformation, we can apply the simple linear models such as recursive least-squares and linear minimum variance estimators for the prediction of the suspended sediment concentration. These methods indicate that the coefficients in the sediment-rating curve are time varying. These methods can estimate sediment discharge by identifying the coefficients in the sed...

Discussion of “Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers”

Citation Abrahart, R.J. & Mount, N.J. (2011) Discussion of “Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers by S.A. Mirgagheri et al. (2010, Hydrol. Sci. J. 55(7), 1175–1189).” Hydrol. Sci. J. 56(7), 1325–1329.

Flow-driven soil erosion processes and the size selectivity of sediment

Summary The processes and mechanisms of entrainment and transportation of soil particles by surface runoff was investigated in the laboratory by a 0.05 × 0.2 × 3 m flume for two contrasting soil samples. The results show that there are at least two different mechanisms affecting particle transport by flow, with their relative importance relating to hydraulic condition, especially flow streampower. Different particle size classes are transported mainly by one mechanism. Suspension/saltation was observed to be the only mechanism at low streampowers ( −2 ). Bed load transport, probably involving rolling was shown to be activated at the streampowers greater than a specific threshold of about 0.1–0.15 W m −2 , becoming the dominant transport mechanism at these modest streampowers. The relative importance of the two transport mechanisms was also related to soil types. Particle density seemed to have a main role in this context. Comparison with theoretical prediction of sediment concentration provided some support for the increasing dominance of bed load transport mechanisms with increasing streampower in these experiments.

Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers

The study of sediment load is important for its implications to the environment and water resources engineering. Four models were considered in the study of suspended sediment concentration prediction: artificial neural networks (ANNs), neuro-fuzzy model (NF), conjunction of wavelet analysis and neuro-fuzzy (WNF) model, and the conventional sediment rating curve (SRC) method. Using data from a US Geological Survey gauging station, the suspended sediment concentration predicted by the WNF model was in satisfactory agreement with the measured data. Also the proposed WNF model generated reasonable predictions for the extreme values. The cumulative suspended sediment load estimated by this model was much higher than that predicted by the other models, and is close to the observed data. However, in the current modelling, the ANN, NF and SRC models underestimated sediment load. The WNF model was successful in reproducing the hysteresis phenomenon, but the SRC method was not able to model this behaviour. In general, the results showed that the NF model performed better than the ANN and SRC models. Citation Mirbagheri, S. A., Nourani, V., Rajaee, T. & Alikhani, A. (2010) Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J. 55(7), 1175–1189.

Suspended sediment concentration prediction by Geno-Kalman filtering

More accurate prediction of suspended sediment concentration will likely lead to more economic hydraulic construction and provide a valuable basis for the optimum operation of water resources. The majority of past models have relied on simple regression analysis relating discharge to concentration. A new adaptive prediction approach termed Geno-Kalman filtering (GKF), combining Genetic Algorithm and Kalman filtering techniques is proposed. The model is formed in three steps. Firstly, discharge and suspended sediment concentration are related by using dynamic linear model. Secondly, an optimum transition matrix relating these two state variables is obtained by Genetic Algorithms (GAs), and an optimum Kalman gain is calculated. Thirdly, Kalman filtering is used to predict the suspended sediment concentration from discharge measurement. The proposed method is applied to measurements at the Mississippi River basin in St. Louis, Missouri, and is found to result in smaller absolute, mean square, relative errors compared to perceptron Kalman filtering. Furthermore, Geno-Kalman filtering method outperforms the perceptron Kalman filtering and least square methods in terms of coefficient of efficiency.

Deriving stage–discharge–sediment concentration relationships using fuzzy logic

Many practical problems in water resources require knowledge of the sediment load carried by the rivers, or of the load the rivers can carry without danger of aggragadation or degradation. Hence, the measurement of sediments being transported by a river is of vital interest for planning and designing of various water resources projects. The conventional methods available for sediment load estimation are largely empirical, with sediment rating curves being the most widely used. The rating relationships based on regression techniques are generally not adequate in view of the inherent complexity of the problem. In this study, a fuzzy logic technique is applied to model the stage–discharge–sediment concentration relationship. The technique has been applied to two gauging sites in the Narmada basin in India. Performance of the conventional sediment rating curves, neural networks and fuzzy rule-based models was evaluated using the coefficient of correlation, root mean square error and pooled average relative (underestimation and overestimation) errors (PARE) of sediment concentration. Comparison of results showed that the fuzzy rule-based model could be successfully applied for sediment concentration prediction as it significantly improves the magnitude of prediction accuracy.

Sensitivity of a Distributed Watershed Simulation Model to Spatial Scale

The results of distributed watershed models could be sensitive to spatial and temporal scales at which inputs and model parameters are aggregated. This paper reports findings of a detailed sensitivity analysis conducted on the U.S. Department of Agriculture’s distributed watershed simulation model, known as the Soil and Water Assessment Tool (SWAT). The Big Creek Watershed, located in southern Illinois, is used for the study. The model is calibrated to improve accuracy of its streamflow and sediment concentration predictions using observed data at two locations in the study watershed. Streamflow and sediment concentrations that are simulated by the calibrated model at various spatial scales of discritization are extracted and compared, and inputs and model parameters responsible for sensitivity of model responses are identified. Several indices that could be used as indicators of model behavior are also derived. In addition, feasibility analysis of SWAT is conducted to see if the watershed simulation model could be used as a component in future decision support models developed to assist in identifying integrative watershed management practices that control agricultural nonpoint source pollutions from watersheds. The major findings of the study are: (1) accuracy of the raw model output (streamflow and sediment yield) is very poor for all delineations indicating the need for careful model calibration; (2) streamflow is relatively insensitive to spatial scale; and (3) sediment generated and sediment that leaves the watershed decreases as spatial scale gets coarser. Unlike the findings of previous studies, sediment yield significantly varies, even when properties of the outlet channel remain practically the same. (4) SWAT’s estimate of sediment yield is sensitive to human activities conducted in subbasins of the watershed, thus indicating the capability of SWAT to evaluate consequences of alternative watershed management practices.

Simulating Watershed Outlet Sediment Concentration using the ANSWERS Model by applying Two Sediment Transport Capacity Equations

Soil erosion is one of the major sources of environmental deterioration and has many impacts on soil and water resources quality. Scientists generally use computational models to simulate the amount of sediment yield from watersheds. One of the leading equations used in these models is the overland flow sediment transport capacity. Owing to the lack of such equations, the streamflow equations have been used widely instead. Some of these equations relatively resulted good estimations of runoff flow sediment concentration. In this study the Areal Non point Source Watershed Environment Response Simulation (ANSWERS) model code was changed and the original equation of the model replaced with a new equation. Although the new equation underestimates sediment concentration, the original model resulted in closer agreement between observed and simulated sediment concentration in different rainfall events. Results of this study suggested that adding some components considering fine particles of soil such as silt and clay to the new and original equations, may improve the accuracy of prediction of sediment concentration by the ANSWERS model. Although, both equations revealed that tend to underestimate sediment concentration; however, the original equation overestimated sediment concentration whenever the runoff coefficient exceeded 0·3 under relatively moderate rainfall intensity. Furthermore, the results showed that soil moisture conditions, rainfall depth and rainfall intensity affect underestimation or overestimation of the model; and initial soil moisture is a key factor in simulation of sediment concentration. Wet and dry soil conditions caused overestimation and underestimation of sediment concentration for the original model, respectively. Nevertheless, no specific erosion model is available now which can simulate sediment yield accurately.

River suspended sediment modelling using a fuzzy logic approach

AbstractCorrect estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. This study provides forecasting benchmarks for sediment concentration prediction in the form of a numerical and graphical comparison between fuzzy and rating‐curve models. Benchmarking was based on a 5‐year period of continuous streamflow and sediment concentration data of Quebrada Blanca Station operated by the United States Geological Survey. The benchmark results showed that the fuzzy model was able to produce much better results than rating‐curve models. The fuzzy model proposed in the study is site specific and does not simulate the hysteresis effects. Copyright © 2006 John Wiley & Sons, Ltd.

Sediment Load and Suspended Sediment Concentration Prediction

Sediment is a natural component of riverine environments and its presence in river systems is essential. However, in many ways and many places river systems and the landscape have been strongly affected by human activities which have destroyed naturally balanced sediment supply and sediment transport within catchments. As a consequence a number of severe environmental problems and failures have been identified, in particular the link between sediments and chemicals is crucial and has become a subject of major scientific interest. Sediment load and sediment concentration are therefore highly important variables that may play a key role in environment quality assessment and help to evaluate the extent of potential adverse impacts. This paper introduces a methodology to predict sediment loads and suspended sediment concentrations (SSC) in large European river basins. The methodology was developed within an MSc research study that was conducted in order to improve sediment modelling in the GREAT-ER point source pollution river modelling package. Currently GREAT-ER uses suspended sediment concentration of 15 mg/l for all rivers in Europe which is an obvious oversimplification. The basic principle of the methodology to predict sediment concentration is to estimate annual sediment load at the point of interest and the amount of water that transports it. The amount of transported material is then redistributed in that corresponding water volume (using the flow characteristic) which determines sediment concentrations. Across the continent, 44 river basins belonging to major European rivers were investigated. Suspended sediment concentration data were collected from various European basins in order to obtain observed sediment yields. These were then compared against the traditional empiric sediment yield estimators. Three good approaches for sediment yield prediction were introduced based on the comparison. The three approaches were applied to predict annual sediment yields which were consequently translated into suspended sediment concentrations. SSC were predicted at 47 locations widely distributed around Europe. The verification of the methodology was carried out using data from the Czech Republic. Observed SSC were compared against the predicted ones which validated the methodology for SSC prediction.

A modification to the Wallingford rational regime theory

The rational regime formulation, which was used by White, Bettess and Paris to predict the stable geometry of wide, trapezoidal alluvial channels and in producing the Wallingford Tables for the Design of Stable Alluvial Channels (Hydraulics Research, Wallingford, 1981), is reviewed. This approach (the Wallingford rational regime theory) is known to provide useful results for moderate-scale natural channels but deviates more widely from observed behaviour for laboratory channels and very large rivers. To explore possible reasons for this, wide and narrow trapezoidal channels have been examined in terms of wetted perimeter, hydraulic radius and side slope, and have been compared with equal-area rectangular channels. The effect of the hydraulic radius, via the threshold of motion, on the sediment transport prediction was studied. It is shown that the hydraulic radius associated with selection of side slope has a significant effect on the sediment concentration prediction. The threshold of motion is also an issue. The original method was modified by approximating the channel cross-section as an equivalent area trapezoidal section. Based on the results, an adjustment to the method in terms of hydraulic radius and threshold of motion is proposed. Laboratory flume and field data are used to examine the improved predictions of the modified method.

A modification to the Wallingford rational regime theory

The rational regime formulation, which was used by White, Bettess and Paris to predict the stable geometry of wide, trapezoidal alluvial channels and in producing the Wallingford Tables for the Design of Stable Alluvial Channels (Hydraulics Research, Wallingford, 1981), is reviewed. This approach (the Wallingford rational regime theory) is known to provide useful results for moderate-scale natural channels but deviates more widely from observed behaviour for laboratory channels and very large rivers. To explore possible reasons for this, wide and narrow trapezoidal channels have been examined in terms of wetted perimeter, hydraulic radius and side slope, and have been compared with equal-area rectangular channels. The effect of the hydraulic radius, via the threshold of motion, on the sediment transport prediction was studied. It is shown that the hydraulic radius associated with selection of side slope has a significant effect on the sediment concentration prediction. The threshold of motion is also an issue. The original method was modified by approximating the channel cross-section as an equivalent area trapezoidal section. Based on the results, an adjustment to the method in terms of hydraulic radius and threshold of motion is proposed. Laboratory flume and field data are used to examine the improved predictions of the modified method.

Sediment Concentration and Its Prediction by Perceptron Kalman Filtering Procedure

Predictions of the discharge and the associated sediment concentration are very useful ingredients in any water resources reservoir design, planning, maintenance, and operation. Although there are many empirical relationships between the discharge and sediment concentration amounts, they need estimation of model parameters. Generally, parameter estimations are achieved through the regression method (RM), which has several restrictive assumptions. Such models are locally valid and their structures and parameter values are questionable from region to others. This paper proposes a new approach for sediment concentration prediction provided that there are measurements of discharge and sediment concentration. The basis of the methodology is a dynamic transitional model between successive time instances based on two variables, namely, discharge and sediment concentration measurements. The transition matrix elements are estimated from the measurements through a special form of the artificial neural networks as perceptrons. The sediment concentration predictions from discharge measurements are achieved through a perceptron Kalman filtering (PKF) technique. In the meantime, this technique also provides temporal predictions. A certain portion of the measurement sequence is employed for the model parameter estimations through training and the remaining part is used for the model verification. Detailed comparisons between RM and PKF approaches are presented and, finally, it is shown that the latter model works dynamically by simulating the observation scatter diagram in the best possible manner with smaller prediction errors. The application of the methodology is performed for the discharge and sediment concentration measurements obtained from the Mississippi River basin at St. Louis, Missouri. It is found that the PKF methodology has smaller average relative, root-mean-square, and absolute errors than RM. Furthermore, graphical representation, such as the scatter and frequency diagrams, indicated that the PKF approach has superiority over the RM.

GRIEROM: GRID–BASED VARIABLE SOURCE AREA SOIL–WATER EROSION AND DEPOSITION MODEL

Erosion in a variable source area and the sediment in surface runoff have a major impact on soil conservationand water quality. It is necessary to locate the soil erosion areas and the paths of sediment transport efficiently to alleviatepotential soil loss problems and protect water quality. The objective of this study was to predict temporal variations and spatialdistributions of sediment transport in a variable source area for storm events. A GRIdbased soilwater EROsion anddeposition Model (GRIEROM) is described. This model uses ASCIIformatted map data supported by the GRASS GeographicInformation Systems (GIS) and generates distributed results such as sediment concentration and flux in overland flow areas.GRISTORM was adopted for the hydrological processes. A processbased soil erosion concept was adopted to simulatesoilwater erosion and deposition. The model deals separately with rill and sheet flow and handles vegetation in terms of soilcover. The model was applied to a 170 ha watershed located in the Northern Catskill region of New York. The initial soilmoisture conditions for storm events were based on calibrated values from a Soil Moisture Routing (SMR) model and wereadjusted to include soil moisture variations within a day. Predicted flows from four storm events in 1993 were compared withobserved flows at the watershed outlet for hydrologic calibration. The average NashSutcliffe efficiency for predicting flowat the outlet using calibrated data was 0.847. Parameters related to sheet/rill flow on bare soil were more sensitive than thoseon vegetated soil. Sediment depositability (
) proved to be the most sensitive parameter, followed by the low value of soilerodibility (). The NashSutcliffe efficiencies for predicting sediment yield at the outlet for the two storms were negativebecause of additive prediction and time error of sediment concentration, respectively. The average NashSutcliffe efficiencyfor another two storms was 0.476. The temporal variations and spatial distributions of overland flow and sediment areas werepresented using GRASS. These presentations suggest that most sediment was transported to the stream from the areas thathad high overland flow velocities.

Supply‐based models of suspended sediment transport in streams

Sediment supplies and stream discharge together determine the patterns, over time, of suspended sediment loads in small streams. Most of the uncertainty in empirical streamflow‐sediment relationships can be attributed to changing supplies. Our transport model utilizes a power function of the form C = aQb, where C and Q are sediment concentration and stream discharge, respectively. This expression was augmented with a variable S representing sediment storage in the channel system. The resulting supply‐based model was calibrated to concentration and streamflow time series data from four storm events in a small forested watershed in coastal Oregon. We also calibrated the model to data from a controlled reservoir release in Utah, during which streamflow was held constant for an extended period. In all cases the supply‐based model followed observed concentration time series more accurately than did a transport model based on Q alone. We further enhanced performance of the supply‐based model by distributing sediment supplies S among several compartments which were accessed at different levels of stream discharge. Both the single‐compartment and distributed models demonstrate that a knowledge of sediment supplies can improve predictions of suspended sediment concentrations during storm runoff.

Unit stream power equations for total load

A dimensionless unit stream power equation for the computation and prediction of total sediment concentration is obtained without using any criterion for incipient motion. This equation is compared with a similar dimensionless unit stream power equation proposed by the author in 1973 with the inclusion of criteria for incipient motion. Comparisons between the measured results from laboratory flumes and natural rivers and the computed results from these two unit stream power equations indicate that they are equally accurate in predicting the total sediment concentration in the sand size range. For the 1259 sets of data compared, the average computed result from these equations is only 3% higher than the measured result.