期刊文献+

基于数据分析技术的水文组合预报应用研究 被引量:6

Research on applications of data analysis technology to hydrological combined forecasting
下载PDF
导出
摘要 水文组合预报方法是对多种预报模型的预报结果进行组合分析的一种预报方法.针对历史洪水数据丰富和相对贫乏两种情况,分别提出基于多目标模糊优选和基于贝叶斯分析的组合预报模型.前者是选用多目标模糊优选模型根据各预报方案在不同流量级别下的预报精度确定方案优属度,而后加权平均的组合预报模型;后者则是以贝叶斯分析为基础,同时结合专家经验、马尔可夫蒙特卡罗模拟、Gibbs抽样法,并引入实时校正的组合预报模型.以嫩江流域为实例,分别对两种组合预报模型的精度进行了验证.验证结果表明:两种模型可行而且实用,预报精度均明显高于单个模型的预报精度. Hydrological combined forecasting is a method giving summary and analysis to different forecasting results, produced by different predication models. Aiming at the two situations-abundance or lack of history flood data, the combined forecasting models separately based on multi-objective fuzzy optimization theory and Bayesian analysis theory are proposed correspondingly. The former model introduces multi-objective fuzzy optimization theory to find out optimal relative membership degree of each projection on some precision at different discharges, and then by means of weighted average to confirm the optimal forecasting result; the latter model is based on Bayesian theory, combined with experts' experiences, MCMC simulation, Gibbs sampling and real-time auto-tuning technology. Taking the drainage area of Nenjiang for instance, the precision of the two integrated models was tested, and the result indicates that the established models are available and practical, with higher precision than that of any single model.
出处 《大连理工大学学报》 EI CAS CSCD 北大核心 2007年第2期246-251,共6页 Journal of Dalian University of Technology
基金 国家自然科学基金资助项目(50579095) 大连理工大学青年教师培养基金资助项目(893222)
关键词 水文组合预报 模糊优选 贝叶斯分析 hydrological combined forecasting fuzzy optimization Bayesian analysis
  • 相关文献

参考文献9

  • 1SHAMSELDIN A Y,O'CONNOR K M,LIANG G C,et al.Methods for combining the outputs of different rainfall-runoff models[J].J of Hydrol,1997,197:203-229
  • 2XIONG Li-hua,SHAMSELDIN A Y,O'CONNOR K M.A non-linear combination of the forecasts of rainfall-runoff models by the first-order Takagi-Sugeno fuzzy system[J].J of Hydrol,2001,245:196-217
  • 3SEE L,ABRAHART R J.Multi-model data fusion for hydrological forecasting[J].Comput & Geosci,2001,27:987-994
  • 4SUDHEER K P,NAYAK P C,RAMASASTRI K S.Improving peak flow estimates in artificial neural network river flow models[J].Hydrol Process,2003,17:677-686
  • 5JAIN A,SUDHEER K P,SRINIVASULU S.Identification of physical processes inherent in artificial neural network rainfall runoff models[J].Hydrol Processes,2004,18(3):571-581
  • 6TINGSANCHALI T,GAUTAM M R.Application of tank,NAM,ARMA and neural network models to flood forecasting[J].Hydrol Processes,2000,14:2473-2487
  • 7ABRAHART R J,SEE L.Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments[J].Hydrol Processes,2000,14:2157-2172
  • 8(O)ZELKAN E C,DUCKSTEIN L.Fuzzy conceptual rainfall-runoff models[J].J of Hydrol,2001,253:41-68
  • 9GILKS W S.Markov Chain Monte Carlo in Practice[M].London:Chapman & Hall,1996

同被引文献76

引证文献6

二级引证文献63

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部