大坝渗流观测数据中的混沌现象

探索性研究了大坝渗流观测数据中的混沌现象。文章首先采用互信息量法选择时间延迟τ,然后根据Takens重构相空间理论对大坝渗流观测数据进行相空间重构。作为实例研究,计算出了某水库大坝的渗流观测数据序列的最大Lyapunov指数为正,证明了在大坝渗流观测数据中存在混沌现象。     

流域年径流时序分析的混沌网络模型研究

以混沌理论为基础,对三峡寸滩站月平均径流量时序曲线进行了相空间重构,确定了合理的饱和关联维数.与神经网络结合,用多维相空间建立了网络学习样本和教师值,构造了混沌神经网络分析模型.结果表明:流域年径流序列具有混沌性特征;混沌网络模型预测精度要高于标准BP网络模型,预测结果的绝对误差和相对误差均小于BP网络模型.     

灌区降水量预测的混沌神经网络模型及应用

针对降水时间序列的混沌特性,综合运用混沌理论与神经网络方法的基本原理,通过相空间重构的方法建立了灌区降水量预测的混沌神经网络模型,并给出了计算方法和步骤,从新的角度研究灌区降水预测问题。并将该模型用于濮阳灌区降水量预测,结果表明该模型对降水量的预测准确度较高。     

日含沙量时间序列的混沌识别与预测研究

运用关联指数饱和法和改进的最大Lyapunov指数方法对流域产沙系统进行了混沌识别,结果表明日含量序列具有混沌特性.并以重构相空间的饱和嵌入维数作为神经网络输入层节点数,将混沌理论和神经网络二者有机结合,建立了混沌神经网络模型.将该模型用于黄河上游头道拐水文站汛期日含沙量预测,结果表明,该模型应用在汛期日含沙量预测中具有较高的精度.     

基于Lyapunov指数的观测数据短期预测

本文介绍大坝观测数据的Lyapunov指数预报分析方法，应用混沌方法对大坝时间观测序列数据进行处理，并将这种混沌特性应用于大坝变形预测，根据大坝变形的时间观测数据及计算所得的Lyapunov指数规律，就可计算得到较好的预测结果；并对混沌时间序列相空间重构中的延迟时间间隔和嵌入维数的选取方法进行了讨论；结合实例对Lyapunov指数预测方法进行计算验证。     

参考作物腾发量的混沌性识别及预测

本文应用饱和关联维数法对海河流域张北站从1966~2005年50年的参考作物腾发量序列进行混沌性识别,结果表明该序列存在一定的混沌特性。同时,运用自相关函数法和饱和关联维数法确定了该序列重构相空间的嵌入维数和延迟时间,并在此基础上进行了相空间的重构。建立了混沌局域法预测模型对相空间的演化进行了计算,实现了参考作物腾发量的预测,并与时间序列自回归(AR)模型和基于气象资料的BP神经网络模型预测结果进行了比较。结果表明,预测效果比BP网络模型稍差,但明显优于AR模型。这为解决缺乏气象资料地区参考作物腾发量预测问题提供了新的思路。     

相空间神经网络耦合模型在水文预测中的应用

混沌相空间理论和神经网络用于径流系统的中长期预测,较传统途径可以更多地利用时间序列中包含的丰富信息,更好地揭示水文动力学系统的规律.针对混沌时间序列,结合混沌分析理论和BP神经网络,建立了相空间重构和BP神经网络耦合预测模型.经实例研究初步表明,用神经网络拟合相空间相点演化的非线性关系是可行的;相空间神经网络耦合预测模型在水文中长期预报中的应用是可行的、合理的,有较好的预报精度和应用价值.     

月径流序列混沌特性识别及预测

以混沌理论和相空间重构原理为基础,分析计算大峪水文站1955-2006年月径流序列的最佳延迟时间和嵌入维数;运用最大Lyapunov指数λ10证实大峪月径流序列具有混沌特性,从而建立了基于混沌特性的BP神经网络预测模型。仿真及预测结果表明:该模型预测精度较高,可用于大峪月径流预测。     

加权一阶局域法在边坡位移预测中的应用研究

目前,常用的变形预测分析方法有[1]:确定函数法、统计回归分析法、混沌时间序列分析法等.其中混沌时间序列分析法可以在未直接考虑引起位移变形的有关随机因素的条件下,对历史观测数据进行重构相空间处理,建模比较简单、计算量小、预测精度较高. 研究建立了加权一阶局域法多步预报模型,运用Matlab6.5编制了计算程序,并利用预报模型进行了典型混沌系统的预测和水利工程高边坡位移预测,取得了较好的预测效果.     

加权一阶局域法在边坡位移预测中的应用研究

目前，常用的变形预测分析方法有：确定函数法、统计回归分析法、混沌时间序列分析法等。其中混沌时间序列分析法可以在未直接考虑引起位移变形的有关随机因素的条件下，对历史观测数据进行重构相空间处理，建模比较简单、计算量小、预测精度较高。研究建立了加权一阶局域法多步预报模型，运用Matlab6，5编制了计算程序，并利用预报模型进行了典型混沌系统的预测和水利工程高边坡位移预测，取得了较好的预测效果。     

均匀沙床面动床阻力计算的人工神经网络模型

针对泥沙运动数据含有噪音且样本数据较多的特点,提出采用人工神经网络(ANN)BP模型批学习的训练方法可有效地缩短计算时间、提高训练精度。建立了由能坡、无因次单宽流量和无因次泥沙粒径等3个参数预测水深的结构为3-33-1的冲积河槽动床阻力BP模型。预测结果与实测资料比较表明这个人工神经网络模型的预测精度较高,同时这个模型与21个动床阻力公式的比较表明人工神经网络模型要比传统的回归模型精度高。     

前馈人工神经网络法在大坝安全监控中的应用

应用预测模型来监控大坝复杂的工作性态是一条有效途径。但因大坝的工作条件复杂、影响因素众多，给应用精确的数学模型监控大坝工作性态带来了困难。为此，应用人工神经网络模型隐式的数学表达形式，提出并建立了基于交替学习迭代算法的人工神经网络模型，并结合清江隔河岩水电站的实际，研究了这种模型在大坝基础渗流量及进水闸顶位移预测中的实际应用，其误差收敛快，预报精度较高。通过进一步的研究后，这种模型可望为大坝安全性态的实时在线监控提供有力的技术支持。     

Performance Enhancement of Rainfall Pattern – Water Level Prediction Model Utilizing Self-Organizing-Map Clustering Method

During recent two decades, Artificial Neural Network (ANN) has become one of the most widely used methods in hydrology. One solution for better capturing the existing non-linear and complex nature of data is to develop new hybrid approaches. These hybrid models can be developed in a way that two or more techniques are combined in order to benefit from the advantages of these available approaches and eliminate their limitations. The main scope of this paper is to improve the performance of rainfall-water level modeling by combining ANN with Self Organizing Map (SOM) as an unsupervised clustering method. The proposed method in this study consists of two phases. In the first phase, with the aim of reducing the complexity and dimensionality of input data, a two-step clustering using SOM technique is carried out. Then, in the second phase, separate ANN models are used to model each cluster of data, and final results are obtained by combining the outputs of all models. The proposed new hybrid approach is evaluated using real hydrological data of Johor River. The results of the study indicate that the new proposed SOM-ANN hybrid model has a better performance in daily rainfall-water level forecasting compared to ANN model alone.     

基于支持向量机(SVM)的祁连山典型小流域日降水-径流模拟研究

及时准确的日径流预测在流域水资源的合理规划、利用及管理中具有十分重要的作用。本文以支持向量机(SVM)模型为基础,以祁连山典型小流域-排露沟流域为研究区域,建立了流域日降水-径流模型,对流域未来1~7 d的日径流量进行了模拟预测。为检验SVM模型的有效性,模拟结果与人工神经网络(ANN)模型预测结果进行了对比。结果表明:SVM和ANN均表现出了很高的精度;但相比于传统的ANN模型,SVM模型的预测精度显著提高。表明SVM模型在半干旱山区小流域径流预测中有更好的适用性,可以用于流域中长期日径流预测,是资料有限的条件下中长期日径流预测的有效工具。     

Impact of Normalization and Input on ARMAX-ANN Model Performance in Suspended Sediment Load Prediction

The suspended sediment load in rivers is an important parameter in watershed planning and management. Since daily suspended sediment time series contain linear and nonlinear components, existing prediction models are associated with limitations. Therefore, this study introduces a new hybrid model comprising two commonly used stochastic and nonlinear models. The sediment load is first modeled by an autoregressive-moving average with exogenous terms (ARMAX) model. Subsequently, the ARMAX residuals are modeled with an artificial neural network (ANN). For this purpose, discharge (Q) and sediment (S) are considered as model input parameters. Three modeling scenarios are defined to investigate the impact of data normalization on the hybrid model. The exponential and Box-Cox transformation methods are combined into a new data normalization method called mixed transformation. The performance of these methods is then compared. In addition, the impact of the type and number of input combinations on ARMAX-ANN model accuracy is evaluated. To this end, 12 input combinations and 1331 ARMAX and ANN models are verified. The ARMAX model inputs include S, Q and the white noise disturbance term (e), while the ANN model inputs include the ARMAX model results and residuals. Moreover, the hybrid model’s accuracy is compared with the ARMAX and ANN models.     

Deriving Hedging Rules of Multi-Reservoir System by Online Evolving Neural Networks

This paper presents an online optimization scheme for combined use of Artificial Neural Networks (ANN), hedging policies and harmony search algorithm (HS) in developing optimum operating policies for Tehran water resources system. Past efforts in this area are concentrated on using an offline approach. In that approach, an optimization method is first used to derive a long-term set of optimum reservoir releases. These releases are then used as the target vector for training the ANN model. The online method simultaneously uses the optimization and ANN methods and can adopt objective functions other than minimizing the error indices. Therefore, it requires methods other than the backpropagation for training the ANN model. Hence, under the proposed online approach the application of a heuristic method, such as HS, is inevitable for training the network. This is accomplished by using an optimization-simulation procedure where different objective functions and system constraints could be easily handled. The proposed approach is a novel and efficient method for finding the parameters of hedging policies where earlier methods suffered from high computational costs and the curse of dimensionality. The results show the superiority of the proposed online scheme. Moreover, a surrogate model for the hedging policy is presented, which by adhering to the principle of parsimony is more efficient in large scale systems involving many decision variables.     

Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation

We herein propose a simulation-optimization model for groundwater remediation, using PAT (pump and treat), by coupling artificial neural network (ANN) with the grey wolf optimizer (GWO). The input and output datasets to train and validate the ANN model are generated by repetitively simulating the groundwater flow and solute transport processes using the analytic element method (AEM) and random walk particle tracking (RWPT). The input dataset is the different realization of the pumping strategy and output dataset are hydraulic head and contaminant concentration at predefined locations. The ANN model is used to approximate the flow and transport processes of two unconfined aquifer case studies. The performance evaluation of the ANN model showed that the value of mean squared error (MSE) is close to zero and the value of the correlation coefficient (R) is close to 0.99. These results certainly depict high accuracy of the ANN model in approximating the AEM-RWPT model. Further, the ANN model is coupled with the GWO and it is used for remediation design using PAT. A comparison of the results of the ANN-GWO model with solutions of ANN-PSO (ANN-Particle Swarm Optimization) and ANN-DE (ANN-Differential Evolution) models illustrates the better stability and convergence behaviour of the proposed methodology for groundwater remediation.

     

A coupled 1-D and 2-D channel network mathematical model used for flow calculations in the middle reaches of the yangtze river

A coupled one-dimensional (1-D) and two-dimensional (2-D) channel network mathematical model is proposed for flow calculations at nodes in a channel network system in this article.For the 1-D model, the finite difference method is used to discretize the Saint-Venant equations in all channels of a looped network.The Alternating Direction Implicit (ADI) method is adopted for the 2-D model at the nodes.In the coupled model, the 1-D model provides a good approximation with small computational effort, while the 2-D model is applied for complex topography to achieve a high accuracy.An Artificial Neural Network (ANN) method is used for the data exchange and the connectivity between the 1-D and 2-D models.The coupled model is applied to the Jingjiang-Dongting Lake region, to simulate the tremendous looped channel network system, and the results are compared with field data.The good agreement shows that the coupled hydraulic model is more effective than the conventional 1-D model.     

Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network

Developing water level forecasting models is essential in water resources management and flood prediction. Accurate water level forecasting helps achieve efficient and optimum use of water resources and minimize flooding damages. The artificial neural network (ANN) is a computing model that has been successfully tested in many forecasting studies, including river flow. Improving the ANN computational approach could help produce accurate forecasting results. Most studies conducted to date have used a sigmoid function in a multi-layer perceptron neural network as the basis of the ANN; however, they have not considered the effect of sigmoid steepness on the forecasting results. In this study, the effectiveness of the steepness coefficient (SC) in the sigmoid function of an ANN model designed to test the accuracy of 1-day water level forecasts was investigated. The performance of data training and data validation were evaluated using the statistical index efficiency coefficient and root mean square error. The weight initialization was fixed at 0.5 in the ANN so that even comparisons could be made between models. Three hundred rounds of data training were conducted using five ANN architectures, six datasets and 10 steepness coefficients. The results showed that the optimal SC improved the forecasting accuracy of the ANN data training and data validation when compared with the standard SC. Importantly, the performance of ANN data training improved significantly with utilization of the optimal SC.     

Simulation of rainfall-underground outflow responses of a karstic watershed in Southwest China with an artificial neural network

Karstic aquifers in Southwest China are largely located in mountainous areas and groundwater level observation data are usually absent. Therefore, numerical groundwater models are inappropriate for simulation of groundwater flow and rainfall-underground outflow responses. In this study, an artificial neural network （ANN） model was developed to simulate underground stream discharge. The ANN model was applied to the Houzhai subterranean drainage in Guizhou Province of Southwest China, which is representative of karstic geomorphology in the humid areas of China. Correlation analysis between daily rainfall and the outflow series was used to determine the model inputs and time lags. The ANN model was trained using an error backpropagation algorithm and validated at three hydrological stations with different karstic features. Study results show that the ANN model performs well in the modeling of highly non-linear karstic aquifers.