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1.
Gene expression programing (GEP) is used to estimate the suspended sediment yield (SSY) in Euphrates River. SSY is considered to be a function of (i) discharge and (ii) time‐lagged discharge and SSY. The proposed models were trained to extrapolate natural stream data collected from five stations in Middle Euphrates Basin. A detailed sensitivity analysis is done to select the time‐lagged discharge and sediment yield variables. GEP implicitly evaluates the contribution of each independent variable on the fitness of candidate solution and eliminates the variable having no contribution. In this study, all input variables are observed to be included in the proposed GEP models, which prove the significance of each variable. Also, standard and modified sediment rating curves and regression‐based formulae are developed for the five stations. In verification, the estimations of GEP formulae agree well with the measured ones. The GEP models are evaluated by the results of the rating curves and regression formulae. In general, the GEP formulae give better results compared to the rating curves and regression‐based formulae. 相似文献
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《水文科学杂志》2013,58(1):183-197
Abstract Abstract Correct estimation of the sediment volume carried by a river is important with respect to pollution, channel navigability, reservoir filling, hydroelectric equipment longevity, fish habitat, river aesthetics and scientific interests. However, conventional sediment rating curves are not able to provide sufficiently accurate results. In this study, models incorporating fuzzy logic are developed as a superior alternative to the sediment rating curve technique for determining the daily suspended sediment concentration for a given river cross-section. 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 five-year period of continuous streamflow and sediment concentration data from the Quebrada Blanca Station operated by the United States Geological Survey (USGS). Nine different fuzzy models were developed to estimate sediment concentration from streamflow. Each fuzzy model has a different number of membership functions. The parameters of the membership functions were found using a differential evolution algorithm. The benchmark results showed that the fuzzy models were able to produce much better results than rating curve models for the same data inputs. 相似文献
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ABSTRACT In order to understand and adequately manage hydrological stress, it is necessary to simulate groundwater levels accurately. In this research, gene expression programming (GEP) and M5 model tree (M5) are used to simulate monthly groundwater levels. The models are combined with wavelet transform to produce two hybrid models: wavelet gene expression programming (WGEP) and wavelet M5 model tree (WM5). For the simulation, groundwater level, temperature and precipitation values from three observation wells and one meteorological station, located in Iran, are used. The results indicate that the hybrid models, WGEP and WM5, lead to a better performance than the simple models, GEP and M5. The performance of the two hybrid models is similar. It is also observed that selecting a suitable time lag for inputs plays an important role in the accuracy of the simple models. The selection of a suitable decomposition level strongly affects the accuracy of hybrid models. 相似文献
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M. Ali Kokpinar A. Burcu Altan-Sakarya S. Yurdagul Kumcu Mustafa Gogus 《水文科学杂志》2013,58(12):2189-2203
AbstractAnalyses of data from reservoir surveys and sediment rating curves are compared to predict sediment yield in three large reservoir watershed areas in Turkey. Sediment yield data were derived from reservoir sedimentation rates and suspended sediment measurements at gauging stations. The survey data were analysed to provide the volume estimates of sediment, the time-averaged sediment deposition rates, the long-term average annual loss rates in the reservoir storage capacity, and the long-term sediment yield of the corresponding watershed areas. Four regression methods, including linear and nonlinear cases, were applied to rating curves obtained from gauging stations. Application of the efficiency test to a power function form of a rating curve with nonlinear regression yielded the highest efficiency values. Based on the analysis of the sediment rating curves, sediment load fluxes were calculated by using average daily discharge data at each gauging station. Comparison of these two sediment yield values for each reservoir showed that the sediment yields from the suspended sediment measurements, SYGS, are 0.99 to 3.54 times less than those obtained from the reservoir surveys, SYRS. The results from the reservoir surveys indicate that all three reservoirs investigated have lost significant storage capacity due to high sedimentation rates. 相似文献
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《水文科学杂志》2013,58(6):1270-1285
Abstract The transport of sediment load in rivers is important with respect to pollution, channel navigability, reservoir filling, longevity of hydroelectric equipment, fish habitat, river aesthetics and scientific interest. However, conventional sediment rating curves cannot estimate sediment load accurately. An adaptive neuro-fuzzy technique is investigated for its ability to improve the accuracy of the streamflow—suspended sediment rating curve for daily suspended sediment estimation. The daily streamflow and suspended sediment data for four stations in the Black Sea region of Turkey are used as case studies. A comparison is made between the estimates provided by the neuro-fuzzy model and those of the following models: radial basis neural network (RBNN), feed-forward neural network (FFNN), generalized regression neural network (GRNN), multi-linear regression (MLR) and sediment rating curve (SRC). Comparison of results reveals that the neuro-fuzzy model, in general, gives better estimates than the other techniques. Among the neural network techniques, the RBNN is found to perform better than the FFNN and GRNN. 相似文献
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MARIA MIMIKOU 《水文科学杂志》2013,58(3):369-383
ABSTRACT The suspended sediment rating curves for six stations on four rivers in western and northern Greece are investigated. For each station the suspended sediment load is a power function of the water discharge, which may be distinguished according to wet and dry seasons; the latter yields higher sediment loads for a given discharge than the former. This is due to the higher erosivity of dry season rainfall compared to wet season rainfall producing the same runoff. All rating curve exponents b lie in the range 2.5–3.5 for the wet and 2.0–3.0 for the dry season and are related to the constants a of the curves by empirical equations. The variation in a and b is explained in terms of the annual precipitation and area of the basin, the hypsometric fall, the main channel length, and the average bedslope of the river from the basin divide to the station, through empirical relationships, which also permit the prediction of rating curves for ungauged basins. 相似文献
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《水文科学杂志》2013,58(4)
Abstract The abilities of neuro-fuzzy (NF) and neural network (NN) approaches to model the streamflow–suspended sediment relationship are investigated. The NF and NN models are established for estimating current suspended sediment values using the streamflow and antecedent sediment data. The sediment rating curve and multi-linear regression are also applied to the same data. Statistic measures were used to evaluate the performance of the models. The daily streamflow and suspended sediment data for two stations—Quebrada Blanca station and Rio Valenciano station—operated by the US Geological Survey were used as case studies. Based on comparison of the results, it is found that the NF model gives better estimates than the other techniques. 相似文献
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AbstractThe quantification of the sediment carrying capacity of a river is a difficult task that has received much attention. For sand-bed rivers especially, several sediment transport functions have appeared in the literature based on various concepts and approaches; however, since they present a significant discrepancy in their results, none of them has become universally accepted. This paper employs three machine learning techniques, namely artificial neural networks, symbolic regression based on genetic programming and an adaptive-network-based fuzzy inference system, for the derivation of sediment transport formulae for sand-bed rivers from field and laboratory flume data. For the determination of the input parameters, some of the most prominent fundamental approaches that govern the phenomenon, such as shear stress, stream power and unit stream power, are utilized and a comparison of their efficacy is provided. The results obtained from the machine learning techniques are superior to those of the commonly-used sediment transport formulae and it is shown that each of the input combinations tested has its own merit, as they produce similarly good results with respect to the data-driven technique employed.
Editor Z.W. Kundzewicz 相似文献
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AbstractAccurate prediction of daily pan evaporation (PE) is important for monitoring, surveying, and management of water resources as well as reservoir management and evaluation of drinking water supply systems. This study develops and applies soft computing models to predict daily PE in a dry climate region of south-western Iran. Three soft computing models, namely the multilayer perceptron-neural networks model (MLP-NNM), Kohonen self-organizing feature maps-neural networks model (KSOFM-NNM), and gene expression programming (GEP), were considered. Daily PE was predicted at two stations using temperature-based, radiation-based, and sunshine duration-based input combinations. The results obtained by the temperature-based 3 (TEM3) model produced the best results for both stations. The Mann-Whitney U test was employed to compute the rank of different input combination for hypothesis testing. Comparison between the soft computing models and multiple linear regression model (MLRM) demonstrated the superiority of MLP-NNM, KSOFM-NNM, and GEP over MLRM. It was concluded that the soft computing models can be successfully employed for predicting daily PE in south western Iran.
Editor D. Koutsoyiannis 相似文献
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《水文科学杂志》2013,58(6)
Abstract Abstract The prediction and estimation of suspended sediment concentration are investigated by using multi-layer perceptrons (MLP). The fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, is used for optimization of the network weights for data from two stations on the Tongue River in Montana, USA. The first part of the study deals with prediction and estimation of upstream and down-stream station sediment data, separately, and the second part focuses on the estimation of downstream suspended sediment data by using data from both stations. In each case, the MLP test results are compared to those of generalized regression neural networks (GRNN), radial basis function (RBF) and multi-linear regression (MLR) for the best-input combinations. Based on the comparisons, it was found that the MLP generally gives better suspended sediment concentration estimates than the other neural network techniques and the conventional statistical method (MLR). However, for the estimation of maximum sediment peak, the RBF was mostly found to be better than the MLP and the other techniques. The results also indicate that the RBF and GRNN may provide better performance than the MLP in the estimation of the total sediment load. 相似文献
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Arthur J. Horowitz 《水文研究》2003,17(17):3387-3409
In the absence of actual suspended sediment concentration (SSC) measurements, hydrologists have used sediment rating (sediment transport) curves to estimate (predict) SSCs for subsequent flux calculations. Various evaluations of the sediment rating‐curve method were made using data from long‐term, daily sediment‐measuring sites within large (>1 000 000 km2), medium (<1 000 000 to >1000 km2), and small (<1000 km2) river basins in the USA and Europe relative to the estimation of suspended sediment fluxes. The evaluations address such issues as the accuracy of flux estimations for various levels of temporal resolution as well as the impact of sampling frequency on the magnitude of flux estimation errors. The sediment rating‐curve method tends to underpredict high, and overpredict low SSCs. As such, the range of errors associated with concomitant flux estimates for relatively short time‐frames (e.g. daily, weekly) are likely to be substantially larger than those associated with longer time‐frames (e.g. quarterly, annually) because the over‐ and underpredictions do not have sufficient time to balance each other. Hence, when error limits must be kept under ±20%, temporal resolution probably should be limited to quarterly or greater. The evaluations indicate that over periods of 20 or more years, errors of <1% can be achieved using a single sediment rating curve based on data spanning the entire period. However, somewhat better estimates for the entire period, and markedly better annual estimates within the period, can be obtained if individual annual sediment rating curves are used instead. Relatively accurate (errors <±20%) annual suspended sediment fluxes can be obtained from hydrologically based monthly measurements/samples. For 5‐year periods or longer, similar results can be obtained from measurements/samples collected once every 2 months. In either case, hydrologically based sampling, as opposed to calendar‐based sampling is likely to limit the magnitude of flux estimation errors. Published in 2003 John Wiley & Sons, Ltd. 相似文献
14.
M. B. Collins 《地球表面变化过程与地形》1981,6(6):517-539
The transport of sediment from six small (0.2 to 17.6 km2) headwater catchments is described. The catchments under investigation were located in relation to predominant lithological deposits within the Cretaceous rock succession; two of the areas were underlain by (Weald) clay, two by sandstone (Ashdown Sand and Tunbridge Wells Sand) and two by chalk. The climate of the region under investigation is temperate, with an average annual precipitation (850 mm) in excess of potential evapotranspiration (450 mm). The transport of suspended material from within the catchments was examined by collecting samples of the water-sediment mixture draining the areas, using hand held depth-integrating and permanently installed stage sampling systems. The results of the regularly maintained sampling programme, over a two-year period, are described. Attempts were made to both measure and compute bed load transport. Suspended sediment concentrations are compared between catchments and related to hydrological characteristics. The nature of the material in transit is examined. Sediment rating curves are derived for each of the headwater catchments, defining the relationship in the form y = Axb (where y = suspended sediment concentration (mg/1) and x = water discharge (m3/s)). Annual rating curves are used to derive annual suspended sediment loads by combination with water discharge data, using a log-incremental computerized approach. Multiple regression techniques have been used to examine annual loads in terms of hydrological and morphological characteristics of the headwater catchments. Based on the field information available, a generalized model for the relationship between suspended sediment concentration and water discharge is described. Finally, the derived annual loads from the headwater catchments are combined with both limited observations from the larger Sussex rivers and data available for other catchment investigations in the British Isles, to produce a series of prediction equations for catchment yield under temperate climatic conditions. 相似文献
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Jonathan A. Warrick 《水文研究》2015,29(6):936-949
Sediment rating curves, which are fitted relationships between river discharge (Q) and suspended‐sediment concentration (C), are commonly used to assess patterns and trends in river water quality. In many of these studies, it is assumed that rating curves have a power‐law form (i.e. C = aQb, where a and b are fitted parameters). Two fundamental questions about the utility of these techniques are assessed in this paper: (i) how well to the parameters, a and b, characterize trends in the data, and (ii) are trends in rating curves diagnostic of changes to river water or sediment discharge? As noted in previous research, the offset parameter, a, is not an independent variable for most rivers but rather strongly dependent on b and Q. Here, it is shown that a is a poor metric for trends in the vertical offset of a rating curve, and a new parameter, â, as determined by the discharge‐normalized power function [C = â (Q/QGM)b], where QGM is the geometric mean of the Q‐values sampled, provides a better characterization of trends. However, these techniques must be applied carefully, because curvature in the relationship between log(Q) and log(C), which exists for many rivers, can produce false trends in â and b. Also, it is shown that trends in â and b are not uniquely diagnostic of river water or sediment supply conditions. For example, an increase in â can be caused by an increase in sediment supply, a decrease in water supply or a combination of these conditions. Large changes in water and sediment supplies can occur without any change in the parameters, â and b. Thus, trend analyses using sediment rating curves must include additional assessments of the time‐dependent rates and trends of river water, sediment concentrations and sediment discharge. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. Hydrological Processes published by John Wiley & Sons Ltd. 相似文献
16.
《国际泥沙研究》2017,(4)
Knowledge of the boundary shear stress distribution in channels is important because it is a key factor affecting on erosion and sedimentation rates. The presence of sediment deposits in sewers is often reported during operation, and circular channels are frequently used in sewer networks. Gene expression programming(GEP) is applied in this study to determine an equation for evaluating the shear stress distribution along the wetted perimeter of a circular channel with a flat bed, because of the presence of sediment on the bed. In view of the parameters affecting the shear stress distribution, five dimensionless parameters are applied to develop six GEP models to be used with 905 experimental data. The impact of the shear stress parameters is studied using the six GEP models and by dividing the wetted perimeter into wall and bed sections. Two equations are extracted from the GEP models' output to estimate wall and bed shear stresses. The best model results are compared with a well-known equation based on the entropy concept. The GEP model predictions of wall and bed shear stresses are very similar to the experimental outcomes, whereas the entropy-based model overestimates the shear stress distribution.The proposed GEP models demonstrate superior performance in estimating the shear stress distribution with a mean absolute percentage error(MAPE) of 3.79% compared to an existing equation with MAPE of 9.52%. 相似文献
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Sediment rating curves are commonly used to estimate the suspended sediment load in rivers and streams under the assumption of a constant relation between discharge (Q) and suspended sediment concentrations (SSC) over time. However, temporal variation in the sediment supply of a watershed results in shifts in this relation by increasing variability and by introducing nonlinearities in the form of hysteresis or a path‐dependent relation. In this study, we used a mixed‐effects linear model to estimate an average SSC–Q relation for different periods of time within the hydrologic cycle while accounting for seasonality and hysteresis. We tested the performance of the mixed‐effects model against the standard rating curve, represented by a generalized least squares regression, by comparing observed and predicted sediment loads for a test case on the Chilliwack River, British Columbia, Canada. In our analyses, the mixed‐effects model reflected more accurate patterns of interpolated SSC from Q data than the rating curve, especially for the low‐flow summer months when the SSC–Q relation is less clear. Akaike information criterion scores were lower for the mixed‐effects model than for the standard model, and the mixed‐effects model explained nearly twice as much variance as the standard model (52% vs 27%). The improved performance was achieved by accounting for variability in the SSC–Q relation within each month and across years for the same month using fixed and random effects, respectively, a characteristic disregarded in the sediment rating curve. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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Abstract This paper investigates for a 25-year period the sediment distribution in a semi-arid Brazilian basin (2 × 104 km2) with a network containing more than 4000 surface reservoirs. The methodology is based on rating curves and fitted parameters derived from field data. The results showed that suspended load corresponded to 70% of the total sediment yield (148 t km-2 year-1). The relatively low contribution of the suspended load (compared with other semi-arid regions) was attributed to the impact of the numerous upstream reservoirs, which retained 235 t km-2 year-1. The micro (<1 hm3), small (1–10 hm3), medium-sized (10–50 hm3), and large or strategic (>50 hm3) reservoirs responded to, respectively, 5, 17, 30 and 48% of the total sediment retention by the reservoir network. This indicates that retention in the non-strategic reservoirs has a positive impact on water availability, since siltation of the strategic reservoirs would be expected to more than double if only such reservoirs existed. Citation Lima Neto, I. E., Wiegand, M. C. &; de Araújo, J. C. (2011) Sediment redistribution due to a dense reservoir network in a large semi-arid Brazilian basin. Hydrol. Sci. J. 56(2), 319–333. 相似文献
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Abstract 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. 相似文献
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Abstract Reservoir silting is one of the principal problems affecting the performance of dams in Algeria from the standpoint of reservoir capacity for storage. Foum El Kherza Reservoir (also known as Foum El Gherza), near Biskra Town, Algeria, is subject to dredging operations with the intent of recovering 70% of its initial storage capacity of 47 hm3 (million cubic metres). The forecasting of sediment volume trapped in the reservoir is essential to plan the future use of this resource and to sustain irrigation for the palm groves characteristic of the region. However, there are currently no sediment data, nor a sediment rating curve, for predicting sediment inflow based on hydrological data. This paper describes the optimization of a cumulative trapped sediment curve for Foum El Kherza Reservoir based on 44 years of daily inflows, by using a spreadsheet optimization tool, Microsoft Excel® Solver to calibrate the cumulative sediment load against the cumulative sediment inflow as documented by eight bathymetric surveys since the dam construction. Editor D. Koutsoyiannis; Associate editor A. Montanari Citation Tebbi, F.Z., Dridi, H., and Morris, G.L., 2012. Optimization of cumulative trapped sediment curve for an arid zone reservoir: Foum El Kherza (Biskra, Algeria). Hydrological Sciences Journal, 57 (7), 1368–1377. 相似文献