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不同设计洪水计算方法的比较 被引量:9

Comparison of calculation methods for design flood
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摘要 通过对香溪河流域历史洪水调查,结合洪水特性与成因分析,确定了洪水洪峰、洪量及其重现期,分析了洪水洪峰、洪量的频率特点,采用流量和暴雨两种方法推求坝址设计洪水表明,由流量推求的坝址洪水介于由暴雨途径的多种方法推算的成果之间,因此,采用由流量推求的洪水成果。古洞口工程运行实践表明,用本文方法推求的设计洪水反映了香溪河流域暴雨径流特性,成果合理。 In this work,peak flow,flood volume and return period of historical floods were determined for the Xiangxi drainage through investigation of historical floods and cause analysis of flood behaviors,and the features of frequency-curves of flood peak and flood volume were examined.Both flow method and rainfall method were used for calculation of the design flood at the dam site,and the calculations show that the flow method produced an estimation within the range estimated by various rainfall methods.Therefore,the estimation by the flow method was finally adopted and recommended to the Gudongkou project.Practical operation of this project has verified that the recommended estimation well reflects the behaviors of flood runoff and storm in the Xiangxi drainage.
作者 刘依松
出处 《水力发电学报》 EI CSCD 北大核心 2012年第6期39-43,共5页 Journal of Hydroelectric Engineering
关键词 水文学 设计洪水 历史洪峰 实测流量 设计暴雨 hydrology design flood peak historical flow measured flood design storm
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  • 1杨旭,栾继虹,冯国章.中长期水文预报研究评述与展望[J].西北农业大学学报,2000,28(6):203-207. 被引量:61
  • 2ASCE Task Committee. Artificial neural networks in hydrology - I :Preliminary concepts [ J]. Journal of Hydrologic Engineering, 2000,5(2) :115-123.
  • 3ASCE Task Committee. Artificial neural networks in hydrology - ll: Hydrological applications [ J ]. Journal of Hydrologic Engineering, 2000,5 ( 2 ) : 124-137.
  • 4Cortes C, Vapnik V. Support-vector networks [ J ]. Machine Learning, 1995,20 ( 3 ) :273-297.
  • 5Vapnik V. The Nature of Statistical Learning Theory [ M ]. New York:Springer Verlag, 1999.
  • 6Smola A J, Schoelkopf B. A tutorial on support vector regression [ J]. Statistics and Computing,2004,14 : 199-222.
  • 7Hsu C W, Chang C C, Lin C J. A Practical Guide to Support Vector Classification [ R]. Technical report, Department of Computer Science and Information Engineering, National Taiwan University , 2003.
  • 8Francis E H Tay, L J Cao. Modified support vector machines in financial time series forecasting [ J]. Neurocomputing,48 (2002), 847-861.

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