门限人工神经网络模型及其在洪水预报中的应用

结合门限自回归模型与人工神经网络模型的建模思想，首次提出这两种方法的耦合模型，即门限人工神经网络模型，新模型的实质是一种分段非线性化的处理方法，是对现有门限模型分段线性化的很好改进。实例计算结果说明，新模型在洪水的预报中是有效的，在各种非线性时序动态预测中具有普遍意义和广泛的实用价值。     

基于神经网络的城市日用水量预测

分析了影响城镇日用水量的非线性因素,利用人工神经网络,选择影响城市日用水量的主要因素。建立城镇日用水量预测模型,并将该模型的预测效果与传统的日用水量模型预测效果进行比较,结果显示该模型的预测精度更高、所需时间更短、更适用于影响因素较多的城市日用水量的预测。     

River Flow Forecasting using Recurrent Neural Networks

Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently, Artificial Neural Networks (ANNs) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANNs to forecast monthly river flows. Two different networks, namely the feed forward network and the recurrent neural network, have been chosen. The feed forward network is trained using the conventional back propagation algorithm with many improvements and the recurrent neural network is trained using the method of ordered partial derivatives. The selection of architecture and the training procedure for both the networks are presented. The selected ANN models were used to train and forecast the monthly flows of a river in India, with a catchment area of 5189 km² up to the gauging site. The trained networks are used for both single step ahead and multiple step ahead forecasting. A comparative study of both networks indicates that the recurrent neural networks performed better than the feed forward networks. In addition, the size of the architecture and the training time required were less for the recurrent neural networks. The recurrent neural network gave better results for both single step ahead and multiple step ahead forecasting. Hence recurrent neural networks are recommended as a tool for river flow forecasting.     

人工神经网络非线性时序模型在水文预报中的应用

首先构造出人工神经网络非线性时序模型 ,然后用该模型进行单变量和多变量时间序列预报研究。为了与传统的随机水文模型对比 ,选择了自回归模型。以日流量序列为例 ,研究结果表明 ,人工神经网络非线性时序模型预报效果不错 ,可以在水文预报中加以应用     

人工神经网络与遗传算法在多泥沙洪水预报中的应用

由于水沙作用机制和演进规律的复杂性，以及河道形态变化等因素，多泥沙洪水预报一直是洪水预报的难点，对高含沙洪水快速、准确的预报是多年来国内外专家十分关注的课题。作者采用具有高度非线性识别能力的人工神经网络与遗传算法相结合的方法，探讨了建立智能预报模型的基本方法，进一步对如何提高预报精度的问题进行了研究，并结合黄河洪水预报实例检验了神经网络模型的可行性。检验结果表明，该方法能够较好地识别多泥沙洪水的演进规律，对水位、流量和含沙量都能进行合理预报。     

A Stochastic Model for Representing Drinking Water Demand at Residential Level

In this study, attention is initially focussed on modelling finely sampled (1 min) residential water demand time series. Subsequently, the possibility of simulating the water demand time series relevant to different time intervals and many users is analysed by using an aggregation approach. A cluster Neyman-Scott stochastic process (NSRP) is proposed to represent the residential water demand and a parameterisation procedureis implemented to respect the cyclical behaviour usually observed in any working day. A validation is performed on the basis of the one-minute datacollected on the water distribution system of Castelfranco Emilia located in the province of Modena (I). The elaborations performed show the validity both of the NSRP model and the parameterisation procedure proposedto represent the residential demand with fine time intervals (up to 5–10 min). On the other hand, when a procedure of aggregation is applied to represent the water demand of a high number of users, the results are nolonger satisfactory since only the mean is preserved while the other statistics, and in particular the variance, are underestimated.     

Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks

The applicability of artificial neural networks (ANN) for modelling of daily river flows in a humid tropical river basin with seasonal rainfall pattern is investigated and the model performance assessed using the commonly adopted efficiency indices. Although the developed model showed satisfactory results for rainy period, the predicted hydrograph for the low flow period deviate from the observed data considerably. The rainfall and discharge data available for modelling is explored using Self Organizing Maps (SOM) and the subset of data having definite relationship between the selected hydrologic variables identified. The alternate approach for modelling of river flows utilising the knowledge from SOM analysis has improved the model results. The results show that ANN models can be adopted for forecasting of river flows in the humid tropical river basins for the monsoon period. Input data exploration using SOM is found helpful for developing logically sound ANN models.     

Joint Application of Artificial Neural Networks and Evolutionary Algorithms to Watershed Management

Artificial neural networks (ANNs) have become common data driven tools for modeling complex, nonlinear problems in science and engineering. Many previous applications have relied on gradient-based search techniques, such as the back propagation (BP) algorithm, for ANN training. Such techniques, however, are highly susceptible to premature convergence to local optima and require a trial-and-error process for effective design of ANN architecture and connection weights. This paper investigates the use of evolutionary programming (EP), a robust search technique, and a hybrid EP–BP training algorithm for improved ANN design. Application results indicate that the EP–BP algorithm may limit the drawbacks of using local search algorithms alone and that the hybrid performs better than EP from the perspective of both training accuracy and efficiency. In addition, the resulting ANN is used to replace the hydrologic simulation component of a previously developed multiobjective decision support model for watershed management. Due to the efficiency of the trained ANN with respect to the traditional simulation model, the replacement reduced the overall computational time required to generate preferred watershed management policies by 75%. The reduction is likely to improve the practical utility of the management model from a typical user perspective. Moreover, the results reveal the potential role of properly trained ANNs in addressing computational demands of various problems without sacrificing the accuracy of solutions.     

黄河口滨海区生态需水量神经网络模型的建立

针对河口与近海生物对环境条件变化响应的非线性和不连续性，以及生态系统所具有的多源性、开放性、耗散性和远离平衡态的复杂特征，利用人工神经网络最新技术，建立了河口滨海区生态需水量与健康生态特征指标问的非线性耦合关系的神经网络计算模型，借助Mat lah工具箱强大功能和自主开发接口，快速实现输入数据的预处理、网络的训练和仿真。     

基于利用回归分析法预报上下游相关洪峰水位

根据实际工作需要,为克服采用传统手段利用上下游洪峰水位相关图法来进行洪水预报所带来的不精确性和任意性,笔者利用数学函数方程和回归分析计算软件,使用excel的图形功能,先绘制样本数据散点图,然后在图上添加趋势线,确定了回归方程类型。根据回归方程类型进行了方程系数率定分析,最终推求出预报黑龙江乌云站洪峰水位和洪峰传播时间的数学回归方程,经实例拟合验证,方程计算结果通过检验,基本满足实际应用要求。     

Estimating the Potential Impacts of Irrigation Water Pricing Using Multicriteria Decision Making Modelling. An Application to Northern Greece

A great challenge of the current European water policy is the implementation of volumetric water pricing in the agricultural sector, especially of Mediterranean countries, where irrigation is a necessary precondition of agricultural production and farmers’ income, but also the major consumer of water. The overall aim of the present work is to develop a methodology that will be suitable for the estimation of the potential environmental, economic and social impacts of irrigation water pricing. For this purpose, Multi-Attribute Utility Theory is implemented in order to simulate agricultural decision making at various water pricing scenarios. Water demand functions are then elicited, by means of the best crop and water allocation (farmers’ decisions) in each scenario. The European Water Framework Directive recommends that any issue concerning water resources management (including water pricing policies) should be developed at the river basin level. In this framework, a cluster analysis is performed to partition the river basin area (namely, Loudias River Basin, located in Northern Greece) into a small number of homogeneous sub-regions. The differential impact of water pricing in each region is then analyzed, and finally, an average water demand function is formulated for the whole river basin.