作物产量预测的BP神经网络模型研究

将人工神经网络技术应用于作物产量预测,建立了拓扑结构为6-5-1的三层BP神经网络模型,并分析了模型计算流程。将该模型应用于人民胜利渠灌区1981—2000年作物产量的实例检验,并与灰色模型的模拟结果进行了比较。结果表明:BP神经网络模型用于作物产量预测简便可行,预测的最大误差为-6.71%,平均误差仅为2.64%,预测精度优于灰色模型。     

两种需水预测影响因素分析方法的比较

针对利用BP人工神经网络进行城市需水预测建模前需要对众多影响因素进行筛选的问题,以广州市为例,利用SPSS软件相关分析和基于灰色关联度的相关分析方法分别进行求解。通过比较分析结果,采用SPSS分析方法的结果作为BP人工神经网络预测模型的输入,建立了基于SPSS相关分析的BP人工神经网络模型。由实例计算结果可以表明,该方法在城市需水预测问题上具有较高的精度。     

大坝变形预测的径向基函数网络模型

在大坝变形预测中,运用人工神经网络模型进行预测分析已较为广泛,目前使用最多的是BP网络模型,但由于存在计算量巨大,且易出现局部极小和收敛慢等缺点,为此建立了大坝变形预测的径向基函数神经网络模型,并与改进的BP网络模型进行比较.实例表明,径向基函数模型具有良好的泛化能力,克服了BP模型的局部极小和收敛慢等缺陷,在预测精度及训练速度方面显著优于BP模型,具有一定的推广价值.     

极限学习机在湖库总磷、总氮浓度预测中的应用

基于传统BP人工神经网络模型训练速度慢、参数选择困难、易陷入局部极值等问题,提出极限学习机(ELM)的水质预测模型。以云南某水库为例,选取NH₃-N、NO₂^--N、NO₃^--N、COD_Mn和水体透明度作为网络输入,TP、TN作为输出, 构建基于ELM的湖库TP、TN预测模型,并将ELM预测结果与传统BP、GA-BP、RBF人工神经网络模型模拟结果进行比较。结果表明,ELM模型预测精度高于传统BP和RBF模型模拟结果,甚至略高于GA-BP模型的预测精度,并且ELM模型具有参数选择简便、训练速度快、不会陷入局部最优值等特点,有着较大的计算优势。     

人工神经网络BP模型在枯季径流量预测中的应用

简述了人工神经网络BP模型，以Excel为技术平台，创建了人工神经网络BP预测模型。应用该模型预测了吉林省农安水文站枯季径流量。结果表明，预测结果合理，精度较高；模型操作简便，有进一步推广价值。     

基于神经网络参数优化的岩体边坡稳定性评价

高陡岩体边坡的稳定性是一个内部存在多种相互联系、相互影响因素的复杂系统,而BP人工神经网络属于非线性动态系统,较适合用于评价高陡岩体边坡稳定性。分析了BP网络模型参数对高陡岩体边坡稳定性评价精度的影响,并提出了对模型参数进行优化,以提高预测精度的若干办法。用一工程实例对参数优化后的BP神经网络在高陡岩体边坡稳定性评价中的应用效果进行了检验。研究表明,用经参数优化的BP人工神经网络模型预测高陡岩体边坡稳定性是可行的,预测结果虽然与实际状态存在一定的误差,但仍可以相对准确地反映边坡稳定状况。     

基于神经网络参数优化的岩体边坡稳定性评价

高陡岩体边坡的稳定性是一个内部存在多种相互联系、相互影响因素的复杂系统,而BP人工神经网络属于非线性动态系统,较适合用于评价高陡岩体边坡稳定性.分析了BP网络模型参数对高陡岩体边坡稳定性评价精度的影响,并提出了对模型参数进行优化,以提高预测精度的若干办法.用一工程实例对参数优化后的BP神经网络在高陡岩体边坡稳定性评价中的应用效果进行了检验.研究表明,用经参数优化的BP人工神经网络模型预测高陡岩体边坡稳定性是可行的,预测结果虽然与实际状态存在一定的误差,但仍可以相对准确地反映边坡稳定状况.     

小波神经网络在柴河水库坝基渗流量预测中的应用

小波神经网络是基于小波变换与人工神经网络的一种前馈型神经网络.文中将小渡神经网络模型应用于柴河水库右坝段坝基渗流量的预测,利用实测资料对其模拟计算结果进行检验.通过与BP神经网络模型的预测结果比较,证明小波网络模型的收敛速度更快、预测精度更高.     

一种非线性组合预测方法在径流测报中的应用

将基于人工神经网络的非线性组合预测方法应用于径流预测中,以1996年太阳沱的洪水资料为例,对已经建立的两个模型,采用三层的BP网络进行组合模拟预测,从多方面分析比较,让明用该种方法能够提高预测精度,结果令人满意：     

基于EPSO-NN模型的大坝安全监控数据处理

针对大坝安全监控神经网络模型中选入因子较多,使网络权值的计算成为高维问题,提出将粒子进化-多粒子群优化算法应用到人工神经网络,建立了EPSO-NN模型。该方法改善了BP神经网络易陷入局部极值和解不稳定的缺点。通过工程实例表明:EPSO-NN模型收敛速度快,求解稳定,预测精度比最小二乘回归和BP-NN高。     

河口网河区水文相关模拟的人工神经网络方法与应用

珠江河口网河区的水文过程具有典型的非线性特征。采用人工神经网络的反误差传播模型，以西，北江径流水位及河口潮流水位为作输入，以顺德市主要水道控制站水位变化作为响应输出，进行相关训练，模拟水位变化过程。     

Hybrid ANN–GA model for predicting turbidity and chlorophyll-a concentrations

The transport and fate of admixtures at coastal zones are driven, or at least modulated, by currents. In particular, in tide-dominated areas due to higher near-bottom shear stress at strong currents, sediment concentration and turbidity are expected to be at maximum during spring tide, while algal growth rate likely is peaking up at slack currents during neap tide. Varying weather and atmospheric conditions might modulate the said dependencies, but the water quality pattern still is expected to follow the dominant tidal cycle. As tidal cycling could be predicted well ahead, there is a possibility to use water quality and hydrodynamic high-resolution data to learn past dependencies, and then use tidal hydrodynamic model for nowcasting and forecasting of selected water quality parameters.This paper develops data driven models for nowcasting and forecasting turbidity and chlorophyll-a using Artificial Neural Network (ANN) combined with Genetic Algorithm (GA). The use of GA aims to automate and enhance ANN designing process. The training of the ANN model is done by constructing input–output mapping, where hydrodynamic parameters act as an input for the network, while turbidity and chlorophyll-a are the corresponding outputs (desired target). Afterward, the prediction is carried out only by employing computed water surface elevation as an input for the trained ANN model. The proposed data driven model has successfully revealed complex relationships and utilized its experiential knowledge acquired from the training process for facilitating the subsequent use of the data driven model to yield an accurate prediction.     

Principle Component Analysis in Conjuction with Data Driven Methods for Sediment Load Prediction

This study investigates sediment load prediction and generalization from laboratory scale to field scale using principle component analysis (PCA) in conjunction with data driven methods of artificial neural networks (ANNs) and genetic algorithms (GAs). Five main dimensionless parameters for total load are identified by using PCA. These parameters are used in the input vector of ANN for predicting total sediment loads. In addition, nonlinear equations are constructed, based upon the same identified dimensionless parameters. The optimal values of exponents and constants of the equations are obtained by the GA method. The performance of the so-developed ANN and GA based methods is evaluated using laboratory and field data. Results show that the expert methods (ANN and GA), calibrated with laboratory data, are capable of predicting total sediment load in field, thus showing their transferability. In addition, this study shows that the expert methods are not transferable for suspended load, perhaps due to insufficient laboratory data. Yet, these methods are able to predict suspended load in field, when trained with respective field data.     

Uncertainty Analysis in Sediment Load Modeling Using ANN and SWAT Model

Sediment load estimation is essential in many water resources projects. In this study, the capability of two different types of model including SWAT as a process-based model and ANNs as a data-driven model in simulating sediment load were evaluated. The issue of uncertainty in the simulated outputs of the two models which stems from different sources was also investigated. Calibration and uncertainty analysis of SWAT were performed using monthly observed discharge and sediment load values and through the application of SUFI-2 procedure. The issue of uncertainty in the ANN model was also accounted for by training a network several times with different initial weights and bias values as well as randomly-selected training and validation sets, each time a network trained. Trying different input variables to find the best and most efficient network structure, it was found that in the forested watershed of Kasilian, adding average daily rainfall or previous values of discharge dose not change the performance of the ANN model significantly. Comparing the results of SWAT and ANN, it was found that SWAT model has a superior performance in estimating high values of sediment load, whereas ANN model estimated low and medium values more accurately. Moreover, prediction interval for the results of ANN was narrower than that of SWAT which suggests that ANN outputs are with less uncertainty.     

Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression

The forecast of the sediment yield generated within a watershed is an important input in the water resources planning and management. The methods for the estimation of sediment yield based on the properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment yield. The focus of present study is to deal with the development of decision tree based M5 Model Tree and wavelet regression models for modeling sediment yield in Nagwa watershed in India. A comparison is also performed with the artificial neural network (ANN) model for streamflow forecasting. The root mean square errors (RMSE), Nash-Sutcliff efficiency index (N-S Index), and correlation coefficient (R) statistics are used for the statistical criteria. A comparative evaluation of the performance of M5 Model Tree and wavelet regression versus ANN clearly shows that M5 Model Tree and wavelet regression can prove more useful than ANN models in estimation of sediment yield. Further, M5 model tree offers explicit expressions for use by design engineers.     

非均匀沙卵石推移质输移随机特性研究

使用天然尺度的实验室渠道观测了在恒定流的条件下非均匀卵石床面的推移质运动变化过程，采集到了更高精度、更长时间序列的输沙率变化过程，分析描述了输沙率统计变化对采样时间的依赖性。实测推移质输沙率的脉动现象十分剧烈，且当采样时间越小时，这种脉动现象越剧烈。来流为4300l/s，采样间隔分别为2min、5min与10min情况下，输沙率脉动值较平均值分别大约4.8倍、3.3倍与2.7倍；来流为5500l/s，采样间隔分别为2min、5min与10min情况下，输沙率脉动值较平均值分别大约3.2倍、2.1倍与1.9倍。卵石推移质单宽输沙率的离差系数、无量纲脉动强度以及无量纲极差值均随采样历时与无量纲希尔兹切应力的增大而减小。利用本次试验成果，通过多元线性回归得到的卵石推移质单宽输沙率的离差系数、无量纲脉动强度以及无量纲极差值与采样历时以及无量纲希尔兹切应力的关系式可为工程应用提供参考。     

非均匀沙卵石推移质输移随机特性研究

使用天然尺度的实验室渠道观测了在恒定流的条件下非均匀卵石床面的推移质运动变化过程,采集到了更高精度、更长时间序列的输沙率变化过程,分析了输沙率统计变化对采样时间的依赖性。实测推移质输沙率的脉动现象十分剧烈,且当采样时间越小时,这种脉动现象越剧烈。来流为4300L/s,采样间隔分别为2、5与10min情况下,输沙率脉动值较平均值分别大约4.8倍、3.3倍与2.7倍;来流为5500L/s,采样间隔分别为2、5与10min情况下,输沙率脉动值较平均值分别大约3.2倍、2.1倍与1.9倍。卵石推移质单宽输沙率的离差系数、无量纲脉动强度以及无量纲极差值均随采样历时与无量纲希尔兹切应力的增大而减小。利用本次试验成果,通过多元线性回归得到的卵石推移质单宽输沙率的离差系数、无量纲脉动强度以及无量纲极差值与采样历时以及无量纲希尔兹切应力的关系式可为工程应用提供参考。     

Event-based Sediment Yield Modeling using Artificial Neural Network

In the present study, a back propagation feedforward artificial neural network (ANN) model was developed for the computation of event-based temporal variation of sediment yield from the watersheds. The training of the network was performed by using the gradient descent algorithm with automated Bayesian regularization, and different ANN structures were tried with different input patterns. The model was developed from the storm event data (i.e. rainfall intensity, runoff and sediment flow) registered over the two small watersheds and the responses were computed in terms of runoff hydrographs and sedimentographs. Selection of input variables was made by using the autocorrelation and cross-correlation analysis of the data as well as by using the concept of travel time of the watershed. Finally, the best fit ANN model with suitable combination of input variables was selected using the statistical criteria such as root mean square error (RMSE), correlation coefficient (CC) and Nash efficiency (CE), and used for the computation of runoff hydrographs and sedimentographs. Further, the relative performance of the ANN model was also evaluated by comparing the results obtained from the linear transfer function model. The error criteria viz. Nash efficiency (CE), error in peak sediment flow rate (EPS), error in time to peak (ETP) and error in total sediment yield (ESY) for the storm events were estimated for the performance evaluation of the models. Based on these criteria, ANN based model results better agreement than the linear transfer function model for the computation of runoff hydrographs and sedimentographs for both the watersheds.     

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 2-D Reservoir Routing Model: Sedimentation History of Abbeystead Reservoir, U.K.

A physically based two-dimensional model is applied to the case-study site of Abbeystead Reservoir, U.K. The model, developed fordensity currents, solves the Navier-Stokes equation coupled to ageneral sediment transport equation. Water flow and sediment motion are determined by reference to dimensionless Reynolds andRichardson numbers. An additional dimensionless parameter, a,determines suspended and/or bedload sediment transport rates. Theperformance of the model is considered in relation to data obtained from repeated bathymetric surveys (1876–1991) for Abbeystead Reservoir, which provides an exceptionally detailed long-term record of sedimentation rates and deposition patterns.The suitability of the adopted two-dimensional scheme is relatedto the local morphology of the reservoir and the time-scale of the delta propagation process. First results show good agreementbetween the numerical simulations and the field data. The modelis able to reproduce both the dynamics of delta growth and the quasi-equilibrium delta form reached as the infilling process nears completion.