湘江流域枯水径流时间演变规律及预报研究

The Research on Low Flow of Time Evolution and Forecast in Xiangjiang River

作者: 专业:水文学及水资源 导师:胡国华 年度:2010 学位:硕士  院校: 长沙理工大学

Keywords

Low Flow, Trend analysis, Frequency analysis, Xiangjiang River, Low Flow Forecasting

        本文概述了国内外对枯水研究的主要内容,并以湘江流域湘潭水文站的流量资料为依据,对枯季径流进行了特征分析,并运用Mann-Kendall非参数秩次相关检验法和分段线性回归方法对湘江流域枯季径流进行了趋势分析。然后对枯水径流做了频率分析,最后用支持向量机、投影寻踪回归、BP神经网络建立模型,对湘潭站最小7日平均流量作了预报。主要内容如下:(1)综述目前国内外枯水频率分析和枯水预报方法研究现状,对其进行了系统的分析和总结,提出了本论文研究的目的与意义;(2)通过已有1960~2005年日流量资料采用滑动平均法推求年最小7日平均流量,以湘江流域湘潭站1960-2005年的径流数据为代表,对湘江流域枯水径流趋势进行特征分析,并运用Mann-Kendall非参数秩次相关检验法和分段线性回归方法对湘江流域年最小7日平均流量、年最小日流量、年最小月流量进行了趋势分析,结果表明,这三个指标数据呈上升趋势;(3)本文简单介绍了枯水频率分析常用线型和频率分布线型的选择方法。主要选用P-III分布、对数P-III分布、耿贝尔分布(Gumbel)和对数正态分布(LN)这四种分布线型,对湘江流域湘潭水文站年最小7日平均流量、年最小日平均流量、年最小月平均流量进行频率分析,用PPCC(概率点据相关系数)检验法进行检验。根据检验结果,可以看出P-III分布、耿贝尔分布、对数P-III分布的相关系数都在98%以上,较为理想,比较适合湘江流域枯水频率概率分布计算,其中以P-III分布最为理想,比较方便使用。(4)最后,以湘江流域为研究对象,采用湘江湘潭站45年最小7日平均流量资料建立枯水径流预报模型,先对前40年的年最小7日平均径流资料进行建模训练,然后通过预测模型预测其余年的径流量,以实测径流量进行验证。本文选用投影寻踪模型、支持向量机模型、人工神经网络模型进行预报,从分析的结果看,无论是从合格率还是从均方差、平均绝对相对误差方面,都表明支持向量机模型效果最好,预报精度最高。
    This article has outlined domestic and foreign to the waterless research primary coverage, and take the runoff material in Xiangtan hydrologic station of Xiangjiang River as the basis, has carried on the characteristic analysis to the low flow, and has carried on the trend analysis using Mann-Kendall non-parameter order related inspection method and the piecewise linearity return method to the low flow of Xiangjiang River. Then has made the frequency analysis to the low flow, finally using support vector machine, the projection pursuit regression , the BP neural network to build model, to forecast the 7 daily average current runoff of Xiangtan station. The primary content as follows:1. This article provides the present domestic and foreign low flow frequency analysis and low flow forecast method study situation, carries on system’s analysis summary, and proposed the goal and significance of this paper research.2. Use the glide average method to calculate the smallest 7 daily average runoff, take the1960-2005 year runoff data of Xiangjiang River as representative, carries on the characteristic analysis of the low flow tendency to the Xiangjiang River, and carries on the trend analysis using Mann-Kendall non-parameter order related inspection method and the piecewise linearity return method to the low flow of Xiangjiang River, the result show that the three indicators are of rise trend.3. Studies the low flow probability distribution characteristic of the Xiangjiang River, through probability plot correlation coefficient to select the appropriate theory distribution linear to the Xiangjiang River. According to the results, we can see that the correlation coefficients of P-III distribution, Gumbel distribution, the Log P-III distribution are all above 98%, it is more desirable, and suitable for the low flow frequency calculate of Xiangjiang, result show that the P-III distribution is ideal and convenient.4. Finally, take the Xiangjiang River as the study object, using the 45 year smallest 7 daily average material of Xiangjiang River to establishment low flow prediction model. First, using the previous smallest 40 years year 7 daily average runoff material to training the model, then use the forecast model to forecast the left runoff, then to confirmation the runoff by the actual amount. Through the low flow frequency analysis and the forecast research of the Xiangjiang River, has made certain contribution to Hunan Province’s waterless resources investigation and the research. This text selects the projection pursuit regression, the support vector machine, the BP neural network to build model, the result show that the impact of the support vector machine is the best , and the accuracy is the highest.
        
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