基于BP神经网络的大伙房水库洪水预报模型研究
发布时间：2019-05-09 17:44
【摘要】：大伙房水库作为浑河中上游上的控制性骨干工程,其防洪意义尤为重要。大伙房水库现行的洪水预报模型为大伙房模型(DHF),该模型为集总式模型,由于集总式模型的特性其不考虑水文过程,因此存在对各支流的流量及水位等不能有效描述的问题,而且该模型的参数在选择上是通过优选法选定或人工试错法确定的,并需要实时校正,因此对率定工作者的要求高且率定工作较为繁琐。鉴于以上不足本文综合考虑资料数据的限制、输入数据、边界条件和流域特征等客观因素,建立了一种半分布式BP神经网络洪水预报模型,实际应用中观察发现其易陷入局部最小点,且预报时长较短,因此随后在此基础上对其做出改进,建立了DHF-GA-BP神经网络耦合洪水预报模型,并将建立的模型应用于大伙房坝址以上流域进行洪水预报,该改进模型结合了大伙房模型与BP神经网络的优势,并引入遗传算法,弥补了半分布式BP神经网络模型的不足。本文研究的主要内容及相应结果如下：(1)建立模型前对坝址以上流域进行分区。分区时以距离各个水文站最近的自然流域分水线为界,采用DEM数据与ArcGIS软件最终划分出Ⅰ、Ⅱ和Ⅲ三个区域,北口前水文站所在区为Ⅰ区,占贝水文描述站所在区为Ⅱ区,南章党水文站所在区为Ⅲ区。(2)对将要输入模型中的现有数据进行预处理。利用逐步回归分析法对初始数据进行筛选,保证输入的数据对输出结果有显著影响。筛选结果Ⅰ、Ⅱ区均为8个显著因子、Ⅲ区为6个显著因子、入库断面即全流域为11个显著因子。(3)建立半分布式BP神经网络模型对大伙房水库进行实时洪水预报。预报模型分为两部分,第一部分为子流域流量预报,第二部分为入库断面流量预报,第一部分的输出结果为第二部分的输入数据。结果显示,子流域预报和入库断面预报的结果较好且该模型对子流域内的洪水调度也可起到辅助作用,但在预报精度、模型效率以及预报时长方面仍有待提高,应对其改进。(4)改进模型建立前预先应用遗传算法率定大伙房模型参数,使其可以分别应用于各子流域的洪水预报。检验期预报结果显示,预报效果较好,部分场次虽有不合格现象但整体预报水平仍可满足正式预报需求。(5)针对半分布式BP神经网络模型的缺陷进行改进。应用遗传算法优化原BP神经网络的初始权重及阈值,结合遗传算法率定参数后的大伙房模型的预报成果,形成改进的半分布式BP神经网络模型——DHF-GA-BP神经网络耦合模型。预报结果显示,改进后的洪水预报模型在运行效率和预报精度上都较原模型有所提高,延长了预报时长,且在一定程度上避免了遗传算法率定参数后的大伙房模型预报误差的累积。
[Abstract]:Dahuofang Reservoir is a controlled backbone project in the middle and upper reaches of Hunhe River, and its flood control significance is particularly important. The current flood forecasting model of Dahuofang Reservoir is the Dahuofang model (DHF), which is a lumped model. Because of the characteristics of the lumped model, it does not consider the hydrological process. Therefore, there is a problem that the flow and water level of each tributary can not be effectively described, and the parameters of the model are determined by optimal selection or artificial trial and error method, and need to be corrected in real time. Therefore, the requirements for calibration workers are high and the calibration work is more tedious. In view of the above shortcomings, considering the limitations of data, input data, boundary conditions and watershed characteristics, a semi-distributed BP neural network flood forecasting model is established in this paper. In practical application, it is found that it is easy to fall into the local minimum point and the forecast time is short. Therefore, on this basis, the DHF-GA-BP neural network coupling flood forecasting model is established. The model is applied to flood forecasting above Dahuofang dam site. The improved model combines the advantages of Dahuofang model and BP neural network, and introduces genetic algorithm to make up for the shortcomings of semi-distributed BP neural network model. The main contents and corresponding results of this paper are as follows: (1) before the establishment of the model, the watershed above the dam site is divided. Taking the natural watershed waterline nearest to each hydrologic station as the boundary, the DEM data and ArcGIS software are used to divide the three areas of 鈪，
本文编号：2472963
[Abstract]:Dahuofang Reservoir is a controlled backbone project in the middle and upper reaches of Hunhe River, and its flood control significance is particularly important. The current flood forecasting model of Dahuofang Reservoir is the Dahuofang model (DHF), which is a lumped model. Because of the characteristics of the lumped model, it does not consider the hydrological process. Therefore, there is a problem that the flow and water level of each tributary can not be effectively described, and the parameters of the model are determined by optimal selection or artificial trial and error method, and need to be corrected in real time. Therefore, the requirements for calibration workers are high and the calibration work is more tedious. In view of the above shortcomings, considering the limitations of data, input data, boundary conditions and watershed characteristics, a semi-distributed BP neural network flood forecasting model is established in this paper. In practical application, it is found that it is easy to fall into the local minimum point and the forecast time is short. Therefore, on this basis, the DHF-GA-BP neural network coupling flood forecasting model is established. The model is applied to flood forecasting above Dahuofang dam site. The improved model combines the advantages of Dahuofang model and BP neural network, and introduces genetic algorithm to make up for the shortcomings of semi-distributed BP neural network model. The main contents and corresponding results of this paper are as follows: (1) before the establishment of the model, the watershed above the dam site is divided. Taking the natural watershed waterline nearest to each hydrologic station as the boundary, the DEM data and ArcGIS software are used to divide the three areas of 鈪，
本文编号：2472963
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