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流域水文数据挖掘体系研究 被引量:5

Study on Hydrological Data Mining System of River Basin
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摘要 针对流域水文数据存在的海量、复杂、时空性等一系列特点,面向流域防洪与兴利等主题,建立了以数据层、组织层、挖掘层以及决策层为基础的流域水文数据挖掘体系,并从数据仓库、数据挖掘、元数据管理等几个方面建立了该体系的分析流程,为流域防洪和兴利业务提供了新的解决方案。将数据挖掘体系初步应用于流域的预报和调度中,证明在有充足数据的支持下,形成可行的流域预报和调度方案是合理可行的。随着数据管理、数据分析等技术日益完善,以数据仓库、数据挖掘等技术为基础的水文数据挖掘体系将逐步走向实用化。 As the characteristics of hydrological data of river basin is of massive, complex, temporal and special, we have established hydrological data mining system of river basin which consists of data layer, organizational layer, mining layer and decision-making layer on flood management and water supply, and constructed the analytical flow of this system from the viewpoint of data warehouse, data mining and metadata managemere. The system proposed in this paper is applied to hydrological forecast and operation. With sufficient data, the system is much useful for for mulating feasible hydrological forecast and operation scheme. With the development of data management and analysis technology, hydrological da ta mining system of river basin based on data warehouse and data-mining technology will be applied gradually.
出处 《南水北调与水利科技》 CAS CSCD 2010年第1期61-64,共4页 South-to-North Water Transfers and Water Science & Technology
基金 国家自然科学基金项目(50579095) 大连理工大学青年教师培养基金项目(893222)
关键词 水文数据挖掘 水文数据仓库 水文预报 hydrological data-mining hydrological data warehouse hydrological forecast
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  • 1吴莉萍,朱长军,李莎.灰色预测在地下水位预测中的应用[J].地下水,2012,34(2):66-68. 被引量:9
  • 2李春梅,范全润.空间数据挖掘及其在地理信息系统中的应用[J].楚雄师范学院学报,2005,20(3):6-10. 被引量:7
  • 3赵新生,赵杰,吉俊峰.浅论数据挖掘与水文现代化[J].人民黄河,2005,27(9):28-29. 被引量:4
  • 4罗庆忠.水利工程管理运作过程中存在的问题及改革措施[J].中国科技信息,2007(4):46-47. 被引量:32
  • 5Blatt M, Wiseman S, Domany E. Superparamagnetic clustering of data[J].Physical Review Letters, 1996,76(18):3251-3254.
  • 6Yoon H, Jun S C, Hyun Y, et al. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer[J].Journal of Hydrology, 2011,396(1-2):128-138.
  • 7Mohanty S, Madan K J, Kumar A, et al. Artificial neural network modeling for groundwater level forecasting in a river island of Eastern India[J].Water Resources Management, 2010,24(9):1845-1865.
  • 8Carcano E C, Bartolini P, Muselli M, et al. Jordan recurrent neural network versus IHACRES in modelling daily streamflows[J].Journal of hydrology, 2008,362(3-4):291-307.
  • 9Ding H, Trajcevski G, Scheuermann P, et al. Querying and mining of time series data: Experimental comparison of representations and distance measures[C]// Proceedings of the 34th VLDB. 2008,1(2):1542-1552.
  • 10Yi B K, Faloutsos C. Fast time sequence indexing for arbitrary Lp norms[C]// Proceedings of the 26th International Conference on Very Large Databases. 2000:297-306.

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