兰州大学机构库 >资源环境学院
基于人工神经网络的渭河上游洪水预报研究
Alternative TitleResearch on flood forecasting of the upper Weihe River based on artificial neural network
闵祥宇
Subtype硕士
Thesis Advisor张钰
2011-05-25
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name硕士
KeywordBP神经网络 洪水预报 渭河上游 遗传算法 贝叶斯正则化算法
Abstract防洪是一门预防洪灾造成人民生活和生产损失的学问,因此对河道防洪的研究具有重大意义。由于洪水的产生过程复杂,传统的洪水预报方法需要大量的数据,在资料不足的情况下利用传统的预报方法无法预测。本文依据河道洪水传播的非线性特点,引入BP人工神经网络,建立一种新的河道洪水预报模型。研究河段选用渭河上游武山水文站-北道水文站河段。研究的主要内容如下:(1)简单阐述了当前洪水预报的主要方法,引入BP 神经网络模型,并介绍了BP神经网络在河道洪水预报中的研究现状。以MATLAB(2009a)为模型实现平台,采用1991年-2001年研究区水文站的水位及流量数据,训练及检验BP神经网络模型并保存结果较好的网络,之后以2002年8月洪水为预报样本,定量预测该场洪水的流量。 (2)根据选用水文站数量的不同,研究分为三种模型,以期选出最适合本河段的预报模型。通过基本的BP神经网络模型进行北道站洪水流量预报,结果的确定型系数分别为0.37、0.89、0.95。预报结果证明BP神经网络模型可以用于该河段的洪水流量预报。
Other AbstractFlood control is a kind of knowledge prevention and preventing from flood loss, therefore the research is great significance of river flood control.The generating of floods is complicated, so traditional flood forecasting methods need large amounts of data, it could not be predicted using the traditional forecasting methods in the case of material insufficiency. This paper introducts the BP artificial neural network based on the nonlinear the spread of river flood characteristics and establishes a new river flood forecasting model.The author chose hydrological ~ north word Wushan hydrological inlets of the upstream of Weihe river. The main content of the study are as follows:(1) It briefly expounds the main method of current flood forecasting, introducing the BP nerve network model, and introduces the research status of BP neural network in the river flood forecast. Using MATLAB(2009a) as the model realize platform and water level and hydrological traffic data of 1991-2001, training and test the BP neural network model and saving the results better network, then forecast the flow of the flood in quantitative using the flood of August 2002 as the forecast samples.(2) According to the difference between choosing the number of Hydrological stations, the research is divided into three kinds of models, so as to select the most suitable prediction model of this research.Through the basic BP neural network model for northern road station flood flow forecast, the results of finalize the design coefficients were 0.37, 0.89, 0.95. The forecast results prove the BP neural network model could be used for this section flood flow forecast.
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/241012
Collection资源环境学院
Recommended Citation
GB/T 7714
闵祥宇. 基于人工神经网络的渭河上游洪水预报研究[D]. 兰州. 兰州大学,2011.
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