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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。 相似文献
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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。 相似文献
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浅谈中长期水文预报方法 总被引:4,自引:0,他引:4
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。 相似文献
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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. 相似文献
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River Flow Forecasting using Recurrent Neural Networks 总被引:4,自引:4,他引:0
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. 相似文献
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中长期水文预报方法研究综述 总被引:2,自引:0,他引:2
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。 相似文献
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Hydrologic Data Exploration and River Flow Forecasting of a Humid Tropical River Basin Using Artificial Neural Networks 总被引:2,自引:1,他引:1
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. 相似文献
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Groundwater Level Forecasting in a Shallow Aquifer Using Artificial Neural Network Approach 总被引:6,自引:2,他引:6
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. 相似文献
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为提高天山西部山区融雪径流的预报精度,更好地指导所在区域的工农业生产发展,针对影响预报精度的关键问题(预报因子的选择),基于互信息法、相关系数法、主成分分析法对研究区的预报因子进行优选,采用RBF神经网络以及组合小波BP神经网络模型进行径流预报研究,并进行不同方案的比较。结果表明:①互信息法优选出的预报因子作为模型输入可以提高预报精度;②采用不同优选预报因子作为RBF神经网络以及组合小波BP神经网络模型的输入变量,结果表明RBF神经网络模型的预测精度要好于组合小波BP神经网络模型;③以相对误差作为评价模型精确度的标准,预测效果最好的是基于互信息方法挑选出的预报因子作为RBF神经模型输入数据的模型预测结果。 相似文献
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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。 相似文献