【作者】 王雪；

【导师】 周建中；

【作者基本信息】 华中科技大学 ， 水文学及水资源， 2011， 硕士

【摘要】 水文径流要素由于受到气候、地理条件及人类活动等因素影响,其变化规律错综复杂具有很强的不确定性,除此之外灰色性、模糊性和混沌性等一些复杂特性也存在于该系统中。为了使相关决策部门和领导能够更加及时的统筹安排,获取诸如水力发电、防洪抗旱、蓄水供水等方面更大的经济和社会效益,更加准确地预测未来的径流过程,特别是中长期的径流预报,此时就显得格外关键。本文在中长期径流预报理论研究现状和发展趋势及其系统软件设计开发的基础上,提出了结合小波分析的中长期径流组合预报方法,通过将原始径流序列进行分解与重构,并结合较为成熟的多元线性回归模型、自回归模型、最近邻抽样回归模型以及支持向量机模型,实现了较为精确的中长期径流预报,并针对三峡流域径流预报的实际工程问题,应用长江三峡流域宜昌站年平均径流资料对模型方法进行检验,实例研究表明,本文提出的组合预测方法较为有效,可以显著的提高预报精度。为了使长江三峡中长期径流预报研究成果更好的满足生产单位的需求,改善其作业预报的水平和精度,本文设计开发了具有水文历史数据的管理、检索和查询、径流过程预测与人机交互管理等功能的长江三峡中长期径流预报软件。该软件采用模块化设计,扩展性好,人机界面友好,易于操作。水文信息查询模块实现了降雨、径流及设计洪水等水文数据信息的实时查询功能,同时通过将这些数据录入水文预报专用数据库,实现了水文数据的快速检索及实时更新功能。水文统计分析模块通过对年月径流量进行频率计算和统计特性分析,可使用户方便快捷的得到径流年内年际的统计特性及趋势和周期特性。中长期预报模块由多种预报模型组成,包括多元线性回归模型、自回归小波模型、最近邻抽样回归小波模型等。在常规模型基础上引入小波分析方法,更好的反映了径流的变化特性,使得模型预报效果得到了极大的提高,为三峡梯级防洪和发电调度系统提供了良好的数据支持。更多还原

【Abstract】 Hydrological system which is always influenced by climate, geographical conditions and human activity is a complicated and large-scale nonlinear system, and its change law is also very complicated with lots of uncertainty, randomness, grey, fuzziness and chaos. Runoff prediction especially of the long-term runoff prediction for this system which can make policy-makers to give an overall consideration as soon as possible, is the key link to maximum the beneficial result when conflicts arise between different water-use sectors such as flood control and drought, water stored and wasted and so on.This study firstly gave a detailed review on the current situation and development trend of the long-term runoff prediction and its system software both at home and abroad, and then a new ensemble runoff prediction method based on wave analysis was proposed in this paper. Several models of long-term run-off forecasting are considered, namely Multi-variable linear regression model (MLR), Auto-regressive model (AR), Nearest neighbor bootstrap regressive model (NNBR), Support vector machine model (SVM), Wavelet analysis (WA). Firstly, the original annual runoff series is decomposed into different component sequences with WA, and then the MLR, AR, NNBR and SVM models are used to get the forecast results of different component sequences, at last they are reconstructed with WA again. The observed discharges getting from Yichang station, Yangtze River basin, are used to test the performance of the proposed method. The results confirm that better discharge estimates can be obtained by the multi-model approach based on wavelet analysis compared with the single models.To making the results of this research can be applied to the practical operation of the Three Gorges reservoir, a long-term runoff forecasting system software with a system integration of hydrological information inquiry function, data retrieval and management function and long-term runoff prediction function. This runoff forecasting system software is powerful, user-friendly interface and easy to operate. The entire system integrates hydrological information query and management, hydrological forecasting and hydrological forecasting model base-specific database module, and several other main parts. Hydrological information query and management module enables real-time hydrological data and information inquiry function, and these data into a hydrological forecasting dedicated database for fast retrieval and real-time hydrological forecasting hydrological models provide a powerful data support. Hydrological statistics and analysis module can analyze the frequency and statistics characteristic of yearly and monthly runoff. Long-term runoff prediction module contains several runoff forecasting models, such as WAR model, WNNBR model, WAR-SVM model, WNNBR-SVM model, and so on. The forecasting precision of this module can be observably raised, because the integration of Wavelet analysis method and traditional methods which can better reflects the runoff change characteristics.更多还原