白石水库流域场次洪水水沙模拟研究

发布时间：2018-08-09 16:52

【摘要】：随着社会的进步和人民生活水平的提高,人们对水资源的需求也越来越高；作为城市供水的重要水源,河流、水库的健康生存发展至关重要。泥沙一直以来都是我国水库运用中的重要问题,而且泥沙对河道的危害也很大。泥沙预报可以提前了解泥沙的运动规律,做到防患于未然,将一切损失降到最低,因此泥沙预报就显得至关重要。本文主要针对日益严重的水库、河道淤积问题,以大凌河流域的白石水库作为研究对象,对水库的入库沙量、水库下游站的沙量及沙量过程进行预报。主要研究内容如下： (1)基于BP神经网络的水库入库沙量预报。针对水库淤积日益严重的问题,对入库场次洪水进行分析研究。首先分析了影响入库洪水的各个因素,次降雨、降雨过程、时段降雨以及洪水地区组成与入库沙量之间的关系,然后建立入库沙量预报模型,以最大4小时降雨量、降雨均匀性系数和前期影响雨量作为模型输入变量,初始为随机权重,经过5000次运算进行模拟。结果表明该模型可以有效的对入库沙量进行预报。 (2)基于线性回归和神经网络的下游河道沙量预报。对于河道淤积问题,水库泄洪冲淤是一个常用的方法。利用建库前的历史资料,对水库下游站义县站的沙量进行预报,通过线性回归和神经网络这两种方法进行模拟,两种模型都以上游朝阳站和迷力营子站的洪峰、洪水总量和平均含沙量为输入条件,结果表明,神经网络的预报结果精度更高,其优于线性回归方法。 (3)基于相似推理理论的下游河道沙量过程预报。通过对流域内的历史场次洪水进行相似性分析,得到两两相似的洪水,用平均含沙量指标,由一场洪水含沙量过程去推求另外一场洪水的含沙量过程。首先进行指标相关性分析,根据累计贡献率大于等于85%的前3个指标从而得到新的指标；然后以新的指标对沙量进行模拟。分析可得,由于流域历史资料较少,大部分的洪水过程预测结果一般,对于流域资料全的流域,该方法结果应当会更好。
[Abstract]:With the progress of society and the improvement of people's living standard, people's demand for water resources is higher and higher. As an important source of urban water supply, the healthy survival and development of rivers and reservoirs are very important. Sediment has always been an important problem in the reservoir operation in China, and the harm of sediment to the river is also great. Sediment prediction can be used to understand the movement law of sediment in advance, so as to prevent trouble and minimize all losses, so it is very important to forecast sediment. Aiming at the increasingly serious problem of reservoir and river siltation, this paper takes Baishi Reservoir in Daling River Basin as the research object, forecasts the amount of sediment into reservoir, the quantity of sediment and the process of sediment quantity in downstream station of reservoir. The main contents are as follows: (1) the prediction of reservoir sediment based on BP neural network. Aiming at the increasingly serious problem of reservoir siltation, the flood of reservoir entry site is analyzed and studied. This paper first analyzes the relationship between the factors affecting the inflow flood, the secondary rainfall, the rainfall process, the rainfall during the period, and the composition of the flood area and the amount of sediment in the reservoir, and then establishes the prediction model of the amount of sediment entering the reservoir for the maximum rainfall of 4 hours. The rainfall uniformity coefficient and the early influence rainfall are taken as the input variables of the model, the initial weight is random, and the model is simulated by 5000 operations. The results show that the model can effectively predict the amount of sediment entering reservoir. (2) based on linear regression and neural network, the prediction of sediment quantity in downstream channel can be carried out. Reservoir flood discharge is a common method for river channel siltation. Based on the historical data before the reservoir was built, the sediment volume of Yixian station, a downstream station of the reservoir, was forecasted. The two models were simulated by linear regression and neural network. Both models were based on the Hong Feng of Chaoyang station and Mailiying sub-station in the upper reaches of the reservoir. The results show that the prediction accuracy of the neural network is higher than that of the linear regression method. (3) based on the similarity reasoning theory, the prediction of sediment volume in the downstream channel is predicted. Based on the similarity analysis of the historical floods in the basin, the similar floods are obtained. By using the average sediment content index, the sediment content process of another flood is derived from one flood sediment content process. Based on the first three indexes whose cumulative contribution rate is more than 85%, a new index is obtained, and the new index is used to simulate the sediment quantity. It can be concluded that because of the lack of historical data, most of the flood process prediction results are general, and the result of this method should be better for the basin with all the basin data.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TV122;TV145

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