神经网络模型在中长期水文预报中的实用性探究

人工神经网络因其特有的自组织、自学习和高容错性等功能在解决复杂的非线性问题中得到广泛应用,近年来在水文预测中的应用也越来越多。本文以塔里木河流域叶尔羌河年径流量系列为例,使用人工神经网络与周期叠加分析对叶尔羌河年径流量进行预报,通过对比分析,探究神经网络模型在中长期水文预报中的实用性问题。     

神经网络模型在中长期水文预报中的实用性探究

人工神经网络因其特有的自组织、自学习和高容错性等功能在解决复杂的非线性问题中得到广泛的应用,近年来在水文预测中的应用也越来越多,文章以塔里木河流域叶尔羌河年径流量系列为例,使用人工神经网络与周期叠加分析对叶尔羌河年径流量进行预报,通过对比分析,探究神经网络模型在中长期水文预报中的实用性问题。     

中长期水文预报的预报区间估计方法

中长期水文预报由于影响因素复杂和目前科学水平的限制,还处于探索、发展阶段,预报手段仍以成因分析(物理因子相关)和数理统计方法为主。其中数理统计方法(方差分析、AR(P)模)是我们经常使用的一种重要方法。在使用数理统计方法进行中长期水文预报时,从理论上来说,给定一个预报区间比给定一个具体预报值更为合理。文章就数理统计法进行中长期水文预报如何给定估计合理预报区间进行初步探讨。     

基于ANN模式的中长期水文预报研究

本文以水库月入库径流预测为例进行了中长期水文预报的人工神经网络模式的研究,研究采用了整体模式、分类模式、组合模式等,研究结果表明,具有非线性功能的人工神经网络方法可以改善中长期入库径流的预测。     

Matlab软件在中长期水文预报中的应用

文中简要介绍了M ATLAB语言和BP神经网络并运用于中长期水文预报中,运用M ATLAB神经网络工具箱免去了繁琐的编程工作,取得了良好的效果。     

浅谈中长期水文预报方法

由于影响水文要素的各种因素十分复杂，中长期水文预报方法目前尚不够成熟，因此在做中长期水文预测时必须参考诸多因素，采用多种方法综合分析、合理取值；并应结合当地实际不断积累经验，进一步探讨中长期水文预报方法，以提高水文要素长期预报的精度。     

双重逐步回归分析在中长期水文预报中的应用

文章介绍了中长期水文预报中的双重逐步回归分析的计算步骤,并以大伙房水库的春汛及气象资料为例进行成果验证,对4种预报方法进行了比较。     

水文预报方法与中长期水文预报的分析

文章以辽宁省大伙房水库作为研究对象,通过对几种中长期水文预报方法的比较分析,选取出预报精度最高的一种方法。研究结果表明,BP神经网络法在径流中长期预报方面的精度最高,因此可运用该方法对水库径流进行中长期预报,从而为水库引水及调度计划的合理制定提供依据。期望通过文章的研究能够对中长期水文预报方法的推广应用有所帮助。     

中长期水文预报方法研究综述

从传统的水文统计法、成因分析法和现代的模糊数学方法、人工神经网络方法、灰色系统理论方法等方面,系统地总结了国内外中长期水文预报的研究进展情况。对其研究现状进行了评述和分析,指出了当前中长期水文预报模型存在的模型适用性不是这一主要问题。分析认为:①从物理成因上解释预报因子的合理性,使预报模型建立在严格的物理成因的基础上,是今后中长期水文预报应遵循的基本原则;②根据水文要素变化的非线性特点,将各种方法进行耦合,将在中长期水文预报中发挥重要作用;③应积极开展中长期水文预报所需资料的观测与积累,特别是人类活动的影响、全球气候变暖等引起的预报因子及影响因素的变化。     

影响水文神经网络预报精度的因素探讨

在运用神经网络来进行水文预报过程中,采用不同的参数,对预报效果是有影响的.通过对不同参数组合进行计算,来分析不同系列组合、训练系列长度、数据归一化等对神经网络预报效果的影响.研究发现,不同数据系列组合的预报效果有很大的不同,训练系列长度对预报精度是有影响的,训练数据与预报数据之间存在时间、空间的间隔对预报精度的影响是不确定的,输入数据的归一化处理对预报精度的影响与输入数据的分布区间存在一定关系.     

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.     

积石峡水电站水文预报方案与站网规划设计

为了最大限度地减少暴雨洪水灾害损失,实现积石峡水电站水情测报自动化,根据公伯峡至积石峡区间流域的实际情况,确定了公伯峡～积石峡区间流域水文预报模型,设计了公伯峡至积石峡区间流域水情测报系统站网规划方案,为积石峡水电站水情自动测报系统的建立提供依据,为防汛调度、决策指挥提供科学依据。     

中长期水文预报方法研究综述

由于中长期水文预报对于水资源规划管理、水库及水电站调度具有的重要意义,其研究一直受到学术界和工程界的广泛关注。通过围绕其传统预报方法成因分析、数理统计方法和时间序列分析技术,和其现代的人工智能预报技术,包括模糊分析、人工神经网络、灰色系统分析、小波分析、混沌分析、支持向量机、遗传程序设计以及这些方法的相互耦合等进行的全面介绍和评述,并指出将来中长期水文预报的进一步研究方向。     

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.     

Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach

Forecasting the ground water level fluctuations is an important requirement for planning conjunctive use in any basin. This paper reports a research study that investigates the potential of artificial neural network technique in forecasting the groundwater level fluctuations in an unconfined coastal aquifer in India. The most appropriate set of input variables to the model are selected through a combination of domain knowledge and statistical analysis of the available data series. Several ANN models are developed that forecasts the water level of two observation wells. The results suggest that the model predictions are reasonably accurate as evaluated by various statistical indices. An input sensitivity analysis suggested that exclusion of antecedent values of the water level time series may not help the model to capture the recharge time for the aquifer and may result in poorer performance of the models. In general, the results suggest that the ANN models are able to forecast the water levels up to 4 months in advance reasonably well. Such forecasts may be useful in conjunctive use planning of groundwater and surface water in the coastal areas that help maintain the natural water table gradient to protect seawater intrusion or water logging condition.     

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。     

BP神经网络在水文预报中的应用和展望

简要介绍BP神经网络的发展和特点,从暴雨预报,水位预测,径流分析,水资源配置与管理四个方面综合评述BP神经网络在我国水文预报作业中的应用,并就BP神经网络今后在我国的水文预报作业中的应用进行了研究展望。     

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。     

SCS-CN-based Continuous Simulation Model for Hydrologic Forecasting

A new lumped conceptual model based on the Soil Conservation Service Curve Number (SCS-CN) concept has been proposed in this paper for long-term hydrologic simulation and it has been tested using the data of five catchments from different climatic and geographic settings of India. When compared with the Mishra et al. (2005) model based on variable source area (VSA) concept, the proposed model performed better in all applications. Both the models however exhibited a better match between the simulated and observed runoff in high runoff producing watersheds than did in low runoff producing catchments. Using the results of the proposed model, dominant/dormant processes involved in watershed’s runoff generating mechanism have also been identified. The presented model is found useful in the continuous simulation of rainfall–runoff process in watersheds.