INCIPIENT MOTION OF NON-COHESIVE SEDIMENT UNDER ICE COVER - AN EXPERIMENTAL STUDY

Based on a series of experiments under both ice-covered and free surface conditions, the present article discusses the role of flow velocity and critical shear Reynolds number for incipient motion of bed material. The influence of the resistance coefficients of both the underside of the ice cover and the channel bed on the location of the maximum velocity has been discussed. In addition, the impacts of ice and composite resistance coefficients on flow velocity for incipient motion of bed material have been assessed. The diagram describing the critical shear Reynolds number and the dimensionless shear stress for the incipient motion of sediment under ice covered conditions with different under cover resistance coefficient has been established. The effects of grain size on densimetric Froude number for incipient motion of bed material have been investigated. A relationship between the densimetric Froude number for incipient motion of bed material and the median grain size of bed material as well as the roughness coefficient of channel bed and roughness coefficient of ice cover has been established.     

An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System,Support Vector Machine and Artificial Neural Network Models

Sediment transport in streams and rivers takes two forms as suspended load and bed load. Suspended load comprises sand + silt + clay-sized particles that are held in suspension due to the turbulence and will only settle when the stream velocity decreases, such as when the streambed becomes flatter, or the streamflow into a pond or lake. The sources of the suspended sediments are the sediments transported from the river basin by runoff or wind and the eroded sediments of the river bed and banks. Suspended-sediment load is a key indicator for assessing the effect of land use changes, water quality studies and engineering practices in watercourses. Measuring suspended sediment in streams is real sampling and the collection process is both complex and expensive. In recent years, artificial intelligence methods have been used as a predictor for hydrological phenomenon namely to estimate the amount of suspended sediment. In this paper the abilities of Support Vector Machine (SVM), Artificial Neural Networks (ANNs) and Adaptive Network Based Fuzzy Inference System (ANFIS) models among the artificial intelligence methods have been investigated to estimate the suspended sediment load (SSL) in Ispir Bridge gauging station on Coruh River (station number: 2316). Coruh River is located in the northern east part of Turkey and it is one of the world”s the fastest, the deepest and the largest rivers of the Coruh Basin. In this study, in order to estimate the suspended sediment load, different combinations of the streamflow and the SSL were used as the model inputs. Its results accuracy was compared with the results of conventional correlation coefficient analysis between input and output variables and the best combination was identified. Finally, in order to predict SSL, the SVM, ANFIS and various ANNs models were used. The reliability of SVM, ANFIS and ANN models were determined based on performance criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Efficiency Coefficient (EC) and Determination Coefficient (R²).     

Effect of upward seepage on bedload transport rate

The paper presents an investigation of injection effects on the bedload transport rate. According to dimensional analysis, two dimensionless groups, an Einstein＇s parameter group and a modified densimetric Froude number group, were chosen to examine how injection affects the bedload transport rate. Experimental studies were conducted in an open-channel flume with an upward seepage zone. The sediment particles used for the test were 0.9 mm in diameter. The experimental results show that an increase in the injection velocity causes a reduction in the shear velocity excess, which is defined as the difference between the shear and critical shear velocities, leading to a reduction in the bedload transport rate. The equation for predicting the bedload transport rate in the presence of upward seepage was derived empirically. The proposed prediction method is suitable for engineering practice, since it only requires the undisturbed flow condition, properties of sediment particles, and the injection velocity.     

基于网络的自适应模糊推理系统在冰情预报中的应用

本研究将基于网络的自适应模糊推理系统应用在冰情预报中。通过分析基于网络的自适应模糊推理系统的网络结构、水温数据及其相关预报因子的分布特点、隶属度函数个数及预见期对预报结果影响的比较,确定了隶属度函数类型、隶属度函数个数和预报的预见期。文中以黄河宁蒙河段石嘴山为例,介绍水温预报的应用研究,并将预报水温同实测水温进行比较。通过描述预报值和实测值关系的确定性系数,对预报结果作了分析。结果表明,除了石嘴山水文站2002年预报结果实测值和预报值偏差较大、确定性系数偏小之外,其余预报组次中预报值和实测值均吻合较好,达到甲等预报方案。     

Evaluating the Performance of Agricultural Water Distribution Systems Using FIS,ANN and ANFIS Intelligent Models

Increasing water use efficiency in the agricultural sector requires the use of appropriate methods for intelligent performance evaluation of surface water distribution systems in agriculture. Therefore, in this study a systematic approach was developed for operational performance appraisal of the agricultural water distribution systems. For this purpose, Fuzzy Inference System (FIS), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used to evaluate the technical performance of irrigation network, considering the uncertainties in the water exploitation process. The performance of the developed models was studied on the Roodasht irrigation canal, located in central Iran, which suffers from severe fluctuations in the inflow, by evaluating the adequacy, efficiency, and equity of surface water distribution. Hydraulic simulation of water distribution system, as well as providing the information required for training and validation of the intelligent models, were performed using the HEC-RAS model. The results showed that compared to the FIS model, ANN and ANFIS models similarly predicted the model outputs with lower errors at almost the same level. The adequacy, efficiency, and equity indicators were predicted by ANFIS model with MAPE of 0.16, 0.01 and 0.23, respectively. Also, FIS model was only able to predict the efficiency and could not predict the adequacy and equity with appropriate performance. The findings of this study reveal that since the ANFIS model uses both FIS and ANN models in its structure, it considers the model uncertainty reliably, and it can be used to evaluate the performance of agricultural water systems.

     

水库异重流的三维数值模拟及影响因素分析

不同水沙条件下异重流的潜入位置以及潜入后流速和含沙量的垂向分布规律是研究异重流运动规律的关键要素,对优化水库异重流的排沙调度、提高水库运用效益等至关重要。本文利用SCHISM三维水沙数学模型对水库异重流水槽试验进行了数值模拟,结果表明模型能够得到符合实际情况的垂线流速和含沙量分布,且异重流潜入后的平均厚度、平均流速和平均含沙量均与实测值吻合较好。对异重流模拟结果的分析发现,入口流量和含沙量的增加造成异重流运动速度越快,而入口含沙量的减小和流量的增加会造成异重流厚度增加。异重流垂向流速和含沙量的无量纲化分布受到入口水沙条件的影响,表现在其与断面Froude数的相关性,Froude数越大,则最大流速点与河床的距离越小,最大流速越大;含沙量致密层的厚度越小,对应的含沙量也越大。此外,潜入点位置也受入口水沙条件的影响,表现在潜入水深与入口水深之比随入口Froude数增加而增加的定量关系,由此可以利用入口水沙条件预报异重流潜入的位置。研究结果不仅验证了SCHISM模型用于模拟水库异重流运动的优势,而且丰富和完善了关于异重流潜入点位置及流速和含沙量垂向分布受入口水沙条件影响的变化规律。     

Improving Rainfall Forecasting Efficiency Using Modified Adaptive Neuro-Fuzzy Inference System (MANFIS)

Rainfall is one of the most complicated effective hydrologic processes in runoff prediction and water management. The adaptive neuro-fuzzy inference system (ANFIS) has been widely used for modeling different kinds of nonlinear systems including rainfall forecasting. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different kinds of problems, especially efficient in rainfall prediction. This paper after reconsidering conventional ANFIS architecture brings up a modified ANFlS (MANFlS) structure developed with attention to making ANFIS technique more efficient regarding to Root Mean Square Error (RMSE), Correlation Coefficient (R ²), Root Mean Absolute Error (RMAE), Signal to Noise Ratio (SNR) and computing epoch. The modified ANFIS (MANFIS) architecture is simpler than conventional ANFIS with nearly the same performance for modeling nonlinear systems. In this study, two scenarios were introduced; in the first scenario, monthly rainfall was used solely as an input in different time delays from the time (t) to the time (t-4) to conventional ANFIS, second scenario used the modified ANFIS to improve the rainfall forecasting efficiency. The result showed that the model based Modified ANFIS performed higher rainfall forecasting accuracy; low errors and lower computational complexity (total number of fitting parameters and convergence epochs) compared with the conventional ANFIS model.     

Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design

Obtaining optimal solutions for time-varying groundwater remediation design is a challenging task. A novel procedure first employs input/output data sets obtained by constrained differential dynamic programming (CDDP). Then the Adaptive-Network-Based Fuzzy Inference System (ANFIS), which is a fuzzy inference system (FIS) implemented in the adaptive network framework, is applied to acquire time-varying pumping rates. Results demonstrate that the FIS is an efficient way of groundwater remediation design.     

A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods

Artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have an extensive range of applications in water resources management. Wavelet transformation as a preprocessing approach can improve the ability of a forecasting model by capturing useful information on various resolution levels. The objective of this research is to compare several data-driven models for forecasting groundwater level for different prediction periods. In this study, a number of model structures for Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANN and Wavelet-ANFIS models have been compared to evaluate their performances to forecast groundwater level with 1, 2, 3 and 4 months ahead under two case studies in two sub-basins. It was demonstrated that wavelet transform can improve accuracy of groundwater level forecasting. It has been also shown that the forecasts made by Wavelet-ANFIS models are more accurate than those by ANN, ANFIS and Wavelet-ANN models. This study confirms that the optimum number of neurons in the hidden layer cannot be always determined by using a specific formula but trial-and-error method. The decomposition level in wavelet transform should be determined according to the periodicity and seasonality of data series. The prediction of these models is more accurate for 1 and 2 months ahead (for example RMSE?=?0.12, E?=?0.93 and R ²?=?0.99 for wavelet-ANFIS model for 1 month ahead) than for 3 and 4 months ahead (for example RMSE?=?2.07, E?=?0.63 and R ²?=?0.91 for wavelet-ANFIS model for 4 months ahead).     

于ANFIS的水质评价模型及其应用

在分析以往水质综合评价方法优缺点的基础上,采用自适应神经模糊推理系统(Adaptive Neural FuzzyInference System,ANFIS)建立了水质综合评价模型。以MATLAB为工具,以某市7个水质观测点为实例进行研究,并将评价结果和人工神经网络法、灰色聚类法及地图重叠法的结果进行分析比较。结果表明:该模型具有计算速度快,评价结果客观、合理、稳定等特点,能够有效的应用于水质综合评价。     

模糊神经网络水文预报模型在洺河流域的应用

本文选取雨强距离指数(IPD)及其他有关参数等作为自适应神经模糊推理系统(ANFIS)水文预报模型的输入,提出雨强距离指数的定义及其目的和意义。为剖析雨强距离指数在水文预报模型中的应用,分别建立不包含雨强距离指数的模型A与包含该指数的模型B进行对比评价。结果表明,将雨强距离指数作为ANFIS水文预报模型的输入能够提高模型的预报精度。     

泥沙输移与水流强度指标

利用大量的实验室水槽和天然河道输沙资料，研究了各种水流强度指标与泥沙输沙强度的相关关系。结果表明，水流功率、平均流速、沙粒切应力能较好地预测输沙率而单位水流功率、佛汝德数、Velikanov参数则较好地预测输沙浓度。其中单位水流功率与输沙浓度的关系最佳。     

Online Ensemble Modeling for Real Time Water Level Forecasts

Accurate and reliable flood forecasting is essential to mitigate the threats brought by floods. Ensemble approaches have been used in limited studies to improve the forecasts of component models. In this paper an ensemble model based on neural-fuzzy inference system (NFIS) and three real time updating approaches were used to synthesize the water level forecasts from a Adaptive-Network-based Fuzzy Inference System (ANFIS) model and the Unified River Basin Simulator (URBS) model for three stations in Lower Mekong. The NFIS ensemble model results are compared with the simple average model (SAM) which is adopted as a benchmark ensemble model. The ensemble model of offline learning without real time updating (EN-OFF), ensemble model with real time updating using offline learning (EN-RTOFF), ensemble model with real time updating using online learning (EN-RTON1) and ensemble model with real time updating using online learning and sub-models (EN-RTON2) were studied in this paper. Statistical analysis of the models for all the three stations indicated the superiority of the EN-RTON2 model over EN-RTOFF, EN-RTON1 models, SAM and the EN-OFF model. Not only the spikes in the URBS model were eliminated, but also the time shift problems in the ANFIS model results were decreased.     

异重流潜入运动的剖面二维数值模拟

本文采用变密度流基本方程和混合有限分析法，求解了突扩边界下的异重流潜入运动。通过对数值计算结果分析，讨论了进口密度弗汝德数Fre对潜入运动的影响，加深了对异重流潜入规律的了解。     

A physical,movable-bed model for non-uniform sediment transport,fluvial erosion and bank failure in rivers

Sediment transport processes in rivers continue to pose a challenge when designing movable-bed physical models, particularly for reproducing the grain sorting and bank erosion (fluvial erosion and mass failure). This paper presents and discusses scale effects of a specific scaling approach for multi-grain size mixtures that preserves similarity of initial motion for each grain size class and of the bank stability coefficient between the model and the prototype, but relaxes strict similarity of the Shields and particle Reynolds numbers. This approach is appropriate when bed load transport near incipient motion conditions is being studied, and allows for larger grain size scales than when full Shields parameter similarity is enforced. As part of an environmental project to rehabilitate sediment transport through bank erosion, this method has been applied to scale a Froude number criterion physical model of a reach of the Old Rhine (France). This has resulted in an undistorted scale of 40, and the use of sand as the model bank material. Each grain size has a different geometrical scale. The time scale for sediment motion is grain size and flow discharge dependent. An average time scale of 6 has therefore been used (four model hours = one prototype day). A strategy devised for the field case consists of two higher, larger island groynes that replace the three existing groynes, producing bank erosion for flow rates below the mean annual flow rate. Extrapolation of model behaviour to the prototype is not a major problem, but the volume of eroded bank material may be underestimated, mainly because of the relaxation of the Shields number similarity and the apparent cohesive properties of the model bank material.     

基于自适应神经模糊推理系统（ANFIS）的电力系统短期负荷预测

采用ANFIS（Adaptive Neuro-Fuzzy Interference System）进行电力系统短期负荷预测。ANFIS将模糊理论与神经网络融合，利用神经网络来实现系统的模糊逻辑推理，采用混合学习算法调整前提参数和结论参数，自动产生模糊规则。利用某局网负荷数据对网络进行训练和检测，所得结果表明利用ANFIS预测负荷有效。     

Turbidity current in a circular settling tank.

This paper deals with turbidity currents in a circular settling tank. A mathematical model with a k-epsilon turbulence model has been developed. Using this mathematical model, the following unique properties of turbidity currents in a circular settling tank are demonstrated: turbulence induced by the turbidity currents remains after most sediment particles have settled down. This residual turbulent diffusivity has a serious effect on the settling of finer particles. This phenomenon is a very important result in this study. Especially, in the case of a smaller densimetric Froude number, which is a stronger density effect, this residual turbulence effect increases, and also decreases the removal ratio in the downstream with low concentration. Generally, the bottom density current enhances the sediment transport near the tank bottom, while the bottom shear gives reversal influence. When the settling velocity is high, the settling ends under the developing stage both of the turbidity current and of the bottom boundary layer. On the contrary, if the settling velocity is low, the sediment travels a long distance, where the boundary layer is built up, resulting in the reduction of sediment transport near the tank bottom. The overall properties of the density-affected settling tank are also investigated in terms of the removal ratio.     

浑水异重流潜入理论模型及影响因素研究

现有关于异重流潜入现象物理机制的研究较少,所建立的理论模型普遍只考虑流量和含沙量,事实上,异重流的潜入受多个水力因素共同作用.本文从异重流潜入过渡区的动量方程和能量方程出发,建立理论模型,并结合水槽试验予以模型参数的率定及模型的验证.该理论模型包含流量、含沙量、坡度、阻力系数、断面宽深比等多个水力因素,经分析发现:含沙量增大、流量减小、坡度增大、阻力系数增大、宽深比增大,更易形成异重流.随着含沙量减小,坡度的影响更明显,而阻力系数和宽深比的影响会减弱.     

Investigating a Suitable Empirical Model and Performing Regional Analysis for the Suspended Sediment Load Prediction in Major Rivers of the Aegean Region,Turkey

This study investigates the appropriateness of four major empirical methods [Lane and Kalinske, Einstein, Brooks, Chang—Simons—Richardson] for predicting suspended sediment loads (SSLs) in three major rivers in the Aegean Region, Turkey. The measured data from 1975 to 2005 were used to test performance of the models. It was found that Brooks method was more appropriate, among the others, for predicting suspended sediment loads from each river. The prediction results of Brooks method were further improved by the use of genetic algorithm (GA_Brooks) optimizing a fitting parameter and showing a comparable performance to those of artificial neural networks (ANNs) and neuro-fuzzy (ANFIS) models for the same rivers. GA_Brooks, ANNs, and ANFIS models can be used for predicting loads at a regional scale. The sensitivity analysis results revealed that suspended and bed material particle diameters affect suspended sediment loads significantly.     

A laboratory study of the erosion and transport of cohesive-like sediment mixtures in sewers

Surveys of sewers in the UK have indicated that many sewer systems have significant in-sewer deposits. Many of these existing combined sewers have been constructed at such a gradient and experience such a range of hydraulic conditions that over a period of time they experience repeated phases of sediment deposition, erosion and transport. Deposition of sediment in sewers with its consequent loss of discharge capacity can lead to the surcharging of sewerage systems and the premature operation of combined sewer overflows. The sudden erosion and transport of large quantities of deposited in-sewer sediments during periods of increased flow can significantly contribute to the pollution load imposed on receiving water courses and sewerage treatment plants. It is therefore important not only to be able to estimate the hydraulic performance of sewers but also the conditions under which significant erosion of deposited sediments occur. This paper reports on the rationale behind and the initial results from a laboratory study which aims to investigate the erosion and transport of “cohesive-like” sediment mixtures in controlled laboratory conditions. The choice of the sediments used was aimed at representing the characteristics of sewer sediment mixtures found in the field. These deposits have been found to exhibit a significant degree of cohesion not found in previously studied granular sediment beds.