ASSESSMENT OF BED-LOAD PREDICTORS BASED ON SAMPLING IN A GRAVEL BED RIVER

Bedload transport in alluvial channels has been extensively studied and different equations based on field and/or experimental data have been proposed.Prediction of bed-load transport rate using different equations results in wide ranges which are not always reliable.In this study,some of the universal bedload predictors were evaluated with measured load by a Helley-Smith sampler in the Node River,a gravel bed river in the northeast part of Iran.From 19 sets of data,14 series of data were used to evaluate the bed-load transport equations.The results show that the equations presented by Van Rijn,Meyer-Peter and Mueller,and Ackers and White may adequately predict bedload transport in the range of field data.     

双隐含层BP神经网络模型在老哈河水质预测中的应用

为了快速准确预测老哈河水质,采用老哈河2011-2015年水质监测数据,运用拉格朗日插值法补充缺失值,分别对化学需氧量、生化需氧量、高锰酸盐指数和总磷浓度建立Levenberg-Marquardt优化的双隐含层BP神经网络模型,利用2011-2014的数据建立训练网络,以2015年的数据进行验证与测试。结果表明:五日生化需氧量预测模型,第一隐含层节点数为4,第二隐含层节点数为12时,决定系数0.751 6(P=0.000 3),平均相对误差25.73%;化学需氧量预测模型,第一隐含层节点数为12,第二隐含层节点数为10时,决定系数0.887 5(P0.000 1),平均相对误差27.69%;高锰酸盐预测模型,第一隐含层节点数为6,第二隐含层节点数为3时,决定系数0.854 7(P0.000 1),平均相对误差28.90%;总磷预测模型,第一隐含层节点数为12,第二隐含层节点数为12时,决定系数0.889 2(P0.000 1),平均相对误差17.94%。应用拉格朗日插值法对缺失数据进行补充后建立的双隐含层BP神经网络模型相对误差均小于28.90%,模型的预测效果较好,其中总磷浓度预测效果最好。通过拉格朗日插值,可以建立老哈河赤峰段甸子点位污染指标的双隐含层人工神经网络模型进行水质预测。     

Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration

This study compares two different adaptive neuro-fuzzy inference systems, adaptive neuro-fuzzy inference system (ANFIS) with grid partition (GP) method and ANFIS with subtractive clustering (SC) method, in modeling daily reference evapotranspiration (ET ₀ ). Daily climatic data including air temperature, solar radiation, relative humidity and wind speed from Adana Station, Turkey were used as inputs to the fuzzy models to estimate daily ET ₀ values obtained using FAO 56 Penman Monteith (PM) method. In the first part of the study, the effect of each climatic variable on FAO 56 PM ET ₀ was investigated by using fuzzy models. Wind speed was found to be the most effective variable in modeling ET ₀ . In the second part of the study, the effect of missing data on training, validation and test accuracy of the neuro-fuzzy models was examined. It was found that the ANFIS-GP model was not affected by missing data while the test accuracy of the ANFIS-SC model slightly decreases by increasing missing data’s percent. In the third part of the study, the effect of training data length on training, validation and test accuracy of the ANFIS models was investigated. It was found that training data length did not significantly affect the accuracy of ANFIS models in modeling daily ET ₀ . ANFIS-SC model was found to be more sensitive to the training data length than the ANFIS-GP model. In the fourth part of the study, both ANFIS models were compared with the following empirical models and their calibrated versions; Valiantzas’ equations, Turc, Hargreaves and Ritchie. Comparison results indicated that the three-and four-input ANFIS models performed better than the corresponding empirical equations in modeling ET ₀ while the calibrated two-parameter Ritchie and Valiantzas’ equations were found to be better than the two-input ANFIS models.     

Estimation of Monthly Mean Reference Evapotranspiration in Turkey

Monthly mean reference evapotranspiration (ET ₀ ) is estimated using adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN) models. Various combinations of long-term average monthly climatic data of wind speed, air temperature, relative humidity, and solar radiation, recorded at stations in Turkey, are used as inputs to the ANFIS and ANN models so as to calculate ET ₀ given by the FAO-56 PM (Penman-Monteith) equation. First, a comparison is made among the estimates provided by the ANFIS and ANN models and those by the empirical methods of Hargreaves and Ritchie. Next, the empirical models are calibrated using the ET ₀ values given by FAO-56 PM, and the estimates by the ANFIS and ANN techniques are compared with those of the calibrated models. Mean square error, mean absolute error, and determination coefficient statistics are used as comparison criteria for evaluation of performances of all the models considered. Based on these evaluations, it is found that the ANFIS and ANN schemes can be employed successfully in modeling the monthly mean ET ₀ , because both approaches yield better estimates than the classical methods, and yet ANFIS being slightly more successful than ANN.     

Subset Modeling Basis ANFIS for Prediction of the Reference Evapotranspiration

The study investigates accuracy of a new modeling scheme, subset adaptive neuro fuzzy inference system (subset ANFIS), in estimating the daily reference evapotranspiration (ET₀). Daily weather data of relative humidity, solar radiation, air temperature, and wind speed from three stations in Central Anatolian Region of Turkey were utilized as input to the applied models. The input data set for modeling the ET₀ was divided to several subsets to calibrate the local data using a local modeling-based ANFIS. The estimates obtained from subset ANFIS models were compared with those of the M5 model tree (M5Tree), ANFIS models and ANN. Mean absolute error (MAE), root mean square error (RMSE), and model efficiency factor criteria were applied for analysis of models. The accuracy of M5Tree (from 15.3% to 32.5% in RMSE, from 14.4% to 24.2% in MAE), ANN (from 24.3% to 65.3% in RMSE, from 34.1% to 47% in MAE) and ANFIS (from 17.4% to 35.4% in RMSE, from 10.8% to 28.3% in MAE) models was significantly increased using subset ANFIS for estimating da ily ET_0.     

基于碾压波速的堆石坝压实质量实时监测指标的研究

已有的连续压实质量评价指标在评估堆石坝料的压实质量时仍存在评价精度低、表征压实效果复杂以及结果易受压实材料属性影响等缺点.为给堆石坝施工质量的连续控制提供有效指标,本文采用数据延拓式相关的相位差求解方法来间接获取碾压波速(VR),提出了以实时监测的VR作为堆石坝料压实状态的表征指标.从定性分析角度考虑碾压参数对VR的影...     

推移质平衡输沙率公式研究

将确定性方法和随机性方法相结合,以单位时间内单颗粒泥沙的交换次数代替交换时间,基于颗粒受力平衡推导了推移质颗粒的平均运动速度。结合Bagnold水流功率和Einstein起动概率的基本概念,推导了一个推移质平衡输沙率公式。该公式的系数在临界起动时较小,但随水流强度的增加而增大,从而避免了诸如Meyer-Peter、Einstein、Bagnold、Yalin及Engelund等经典推移质公式难以兼顾低强度和高强度输沙率计算的问题。利用经典的推移质输沙率实测数据检验了本文公式,表明该公式具有较好的适用性。     

Co-Kriging Estimation of Nitrate-Nitrogen Loads in an Agricultural River

Daily nitrate-nitrogen (NO₃-N) loads in the Raccoon River, Iowa, were estimated using Ordinary kriging (OK), Cokriging (CK), and a standard rating curve method (LOADEST) based on a dataset of 3451 measurements of NO₃-N concentration collected over 19 years. The CK estimation utilizes the temporal correlation of NO₃-N load with daily discharge and honors the measured points to improve estimation relative to regression based models. Loads were estimated using the observed concentrations and three subsets of the measured data that correspond to three frequencies (weekly, biweekly, and monthly). Results indicated that daily NO₃-N loads were best estimated by CK using measured loads with daily discharge. Daily load estimates produced by OK using weekly data matched well with measured values, but discrepancies emerged when samples were collected less frequently, e.g., biweekly and monthly. For the entire 19-year dataset, compared to measured loads, the estimated total NO₃-N load decreased using OK when samples were collected monthly, but increased using CK. Load estimation using the seven-parameter LOADEST model did not perform well for the Raccoon River because the correlation of NO₃-N concentration to river discharge was poor. For the site studied, weekly and biweekly sampling may be sufficient to estimate daily NO₃-N loads with CK when daily discharge data is available.     

Application of Several Data-Driven Techniques for Predicting Groundwater Level

In this study, several data-driven techniques including system identification, time series, and adaptive neuro-fuzzy inference system (ANFIS) models were applied to predict groundwater level for different forecasting period. The results showed that ANFIS models out-perform both time series and system identification models. ANFIS model in which preprocessed data using fuzzy interface system is used as input for artificial neural network (ANN) can cope with non-linear nature of time series so it can perform better than others. It was also demonstrated that all above mentioned approaches could model groundwater level for 1 and 2 months ahead appropriately but for 3 months ahead the performance of the models was not satisfactory.     

沙质河床推移质输沙率计算研究

沙质河床推移质输沙计算无论在河流动力学的基本理论研究,还是在工程应用方面都有重要意义。本文回顾了前人主要研究成果,认为因推移质输沙率测验精度低而使现有公式多不适用于典型沙质河床,故在阐明推移质与悬移质泥沙交换运动机理基础上,借助因测验精度较高而在学科研究较成熟的悬移质含沙量垂线分布公式,通过向河底外延推求出推移质泥沙输沙率,并充分考虑泥沙粒径、水流强度及水力摩阻特性对沙质推移质输沙厚度的影响,得到了物理概念清晰、形式较为简明的沙质推移质输沙率公式。通过黄河细沙河床实测资料验证结果表明,本文建立的公式比前人推移质输沙率公式具有较高计算精度,可以应用到推移质输沙率测验精度不能保证的沙质河段的河床变形计算。     

基于人工电场算法优化的大型灌区径流预测模型研究

针对水文预报中径流预测数据序列具有非线性和非平稳性等特点,将一种新型智能优化算法——人工电场算法AEFA与LSTM神经网络结合进行参数优化,建立AEFA-LSTM预测模型,并以赵口大型灌区涡河玄武水文站实测年径流量作为样本数据进行网络优化训练和预测分析,同时与传统优化算法（遗传算法GA和粒子群算法PSO）建立的GA-LSTM和PSO-LSTM预测模型进行对比。结果表明：AEFA-LSTM模型预测值的平均相对误差相较于GA-LSTM模型和PSO-LSTM模型分别降低了7.59%和5.22%,且平均绝对误差MAE、均方误差MSE、均方根误差RMSE均为3种模型中最小,说明所建立的AEFA-LSTM模型可以更高精度地预测径流量,为水文预报提供一种新型高精度径流预测方法。     

Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers

The application of models capable of estimating sediment transport in sewers has been a frequent practice in the past years. Considering the fact that predicting sediment transport within the sewer is a complex phenomenon, the existing equations used for predicting densimetric Froude number do not present similar results. Using Adaptive Neural Fuzzy Inference System (ANFIS) this article studies sediment transport in sewers. For this purpose, five different dimensionless groups including motion, transport, sediment, transport mode and flow resistance are introduced first and then the effects of various parameters in different groups on the estimation of the densimetric Froude number in the motion group are presented as six different models. To present the models, two states of grid partitioning and sub-clustering were used in Fuzzy Inference System (FIS) generation. Moreover, the training algorithms applied in this article include back propagation and hybrid. The results of the proposed models are compared with the experimental data and the existing equations. The results show that ANFIS models have greater accuracy than the existing sediment transport equations.     

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based on stochastic modeling or artificial intelligence (AI) techniques. In this article, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed to forecast the inflow for the Nile River at Aswan High Dam (AHD) on monthly basis. A major advantage of the fuzzy system is its ability to deal with imprecision and vagueness in inflow database. The ANFIS model divides the input space into fuzzy sub-spaces and maps the output using a set of linear functions. A historical database of monthly inflows at AHD recorded over the past 130 years is used to train the ANFIS model and test its performance. The performance of the ANFIS model is compared to a recently developed artificial neural networks (ANN) model. The results show that the ANFIS model was capable of providing higher inflow forecasting accuracy specially at extreme inflow events compared with that of the ANN model. It is concluded that the ANFIS model can be quite beneficial in water management of Lake Nasser reservoir at AHD.     

Reference condition approach to restoration planning

Ecosystem restoration planning requires quantitative rigor to evaluate alternatives, define end states, report progress and perform environmental benefits analysis (EBA). Unfortunately, existing planning frameworks are, at best, semi‐quantitative. In this paper, we: (1) describe a quantitative restoration planning approach based on a comprehensive, but simple mathematical framework that can be used to effectively apply knowledge and evaluate alternatives, (2) use the approach to derive a simple but precisely defined lexicon based on the reference condition concept and allied terms and (3) illustrate the approach with an example from the Upper Mississippi River System (UMRS) using hydrologic indicators. The approach supports the development of a scaleable restoration strategy that, in theory, can be expanded to ecosystem characteristics such as hydraulics, geomorphology, habitat and biodiversity. We identify three reference condition types, best achievable condition (A _BAC), measured magnitude (MM_i which can be determined at one or many times and places) and desired future condition (A_DFC) that, when used with the mathematical framework, provide a complete system of accounts useful for goal‐oriented system‐level management and restoration. Published in 2010 by John Wiley & Sons, Ltd.     

Using United States Geological Survey stream gages to predict flow and temperature conditions to maintain freshwater mussel habitat

Habitat conditions necessary to support freshwater mussels can be difficult to characterize and predict, particularly for rare or endangered species such as the federally endangered dwarf wedgemussel, Alasmidonta heterodon. In this study, we evaluate flow and temperature conditions in three areas of the mainstem Delaware River known to consistently support A. heterodon, and we develop predictive models using the U.S. Geological Survey (USGS) stream gages and thermal stations in order to identify conditions under which habitat alteration could threaten the species. Flow and temperature prediction models based on nearby existing USGS gage and thermal stations were predictive for all three sites. Both discharge prediction and water depth profile models indicate one location (Site 3) was the most vulnerable to low‐flow conditions as it requires the highest discharge rate (26.3 cms) at the USGS Callicoon gage to maintain both the full wetted perimeter (P_full) and minimal wetted perimeter (P_min) and prevent occlusion of areas that contain A. heterodon. Flow management targets aimed at protecting Site 3 should also protect Sites 1 and 2. Although analyses indicated significant benthic habitat available in all three sites even under low discharge rates, specific mussel locations could be vulnerable to dewatering and thermal stress if only P_min values were maintained. Results indicate the magnitude of site temperature deviations from thermal stations varied by site and river temperature. In general, our results suggest that existing temperature and stream gage infrastructure may be used predictively to evaluate the effects of different flow targets on mainstem Delaware River A. heterodon habitat.     

山区河流卵石推移质的输移特性

通过对床面颗粒的受力分析，在岷江都江堰河段和青衣江姜射坝河段的实测资料基础上，导出了卵石推移质运动状态数与爱因斯坦的推移质运动强度函数的关系，能较好地反映四川山区河流卵石推移质时均输沙率与水沙因素的关系，用对数正态分布描述卵石推移质输沙率的随机特性，证明在95％的置信水平下是成立。     

岷江都江堰河段卵石推移质的横向分布特性

常用的推移质输沙率公式多指单位河宽的输沙率计算，对于全断面的输沙率则涉及推移质输沙带的宽度和横向输沙率分布，本文以岷江都江堰河段卵石推移质输沙率的横向分布实测资料为基础，建立了用自然对数函数描述横向分布的概率分布函数，并对横向强输沙区，弱输沙区和不输沙区给出了量化指标。     

泾河年径流量BP神经网络模型研究

以1957—2000年的实测降水序列和泾河年径流量序列为研究对象,利用EMD法和GA建立了泾河年径流量的BP神经网络模型。分析结果表明:泾河流域年降水量变化可能存在准2～3、5～7、10～13、18～22 a的周期;基于EMD的年径流量BP神经网络模型预测值的相对误差为-4.71%～8.21%,基于GA的年径流量BP神经网络模型预测值的相对误差为2.25%～12.22%。     

基于DPSO-ANFIS的大坝变形预测模型

变形预测模型是大坝结构安全性态分析的关键技术支撑。针对现有大坝变形预测模型在精确度、泛化性等方面的不足,将自适应模糊神经网络引入到大坝变形预测模型中,利用动态权重粒子群算法对自适应模糊神经网络中模糊层的适应度值进行参数寻优,形成可以寻找最优适应度值的自适应模糊神经网络,进而建立基于DPSO-ANFIS的大坝变形预测模型。根据大坝原型监测数据,代入训练好的模型得到输出值,并将其与实际监测数据进行对比分析。工程实例应用表明,基于DPSO-ANFIS的大坝变形预测模型输出值与实测值偏差最大为0.0516 mm,均方根误差为0.0351 mm,平均绝对误差为0.0320 mm,各项指标精度均优于基于PSO-ANFIS、ANFIS和BP神经网络的大坝变形预测模型。针对不同位置测点、预测时间段,基于DPSO-ANFIS的大坝变形预测模型输出值接近实测值,预测趋势符合真实值走向,整体预测性能稳定。该模型具有较高的精确度、良好的泛化性与可靠的稳定性,工程实用综合性能较优。     

A Partially-Averaged Navier-Stokes model for hill and curved duct flow

Turbulent flows past hill and curved ducts exist in many engineering applications. Simulations of the turbulent flow are carried out based on a newly developed technique, the Partially-Averaged Navier-Stokes (PANS) model, including separation, recirculation, reattachment, turbulent vortex mechanism. The focus is on how to accurately predict typical separating, reattaching and secondary motion at a reasonable computational expense. The effect of the parameter, the unresolved-to-total ratio of kinetic energy (f_k), is examined with a given unresolved-to-total ratio of dissipation (f_?) for the hill flow with a much coarser grid system than required by the LES. An optimal value of f_k can be obtained to predict the separation and reattachment locations and for more accurate simulation of the resolved turbulence. In addition, the turbulent secondary motions are captured by a smaller f_k as compared with the RANS method with the same grid.