万家寨水库上游的冰情特征分析及预报
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国家自然科学基金项目“冻融过程中湖泊污染物多介质迁移转化规律及机制研究”(51339002),“寒区湖泊冰封期营养物质冰水多介质环境过程及对富营养化影响效应研究”(51369017)


Analysis and Prediction of Ice Characteristics in Upstream of Wajiazhai Dam on Yellow River
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    [目的] 研究黄河万家寨水库上游至托克托县喇嘛湾河段冰情特征及预报方法,为防凌实践提供重要依据。[方法] 通过对1998—2015年度冰情分析,研究了该河段的封、开河过程及二者之间的联系,以野外观测为基础,分别对封、开河时间历时与气温、流量、冰厚、封河天数进行了相关性分析,选取了与封、开河时间相关性系数较高的影响因子,利用BP神经网络模型对万家寨上游河段的封河历时、开河历时、开河日期进行了预报。[结果] 通过与实际观测日期对比,该预报方法合格率达到93%。[结论] 神经网络模型对于黄河万家寨水库上游段封河历时、开河历时、开河日期预报的适用性较强,更适合解决受复杂因素影响的问题。

    Abstract:

    [Objective] The objective of this paper is to explore the characteristics and forecasting methods of ice regime of upper reach of Wanjiazhai dam and Lamawan reach in Togtoh County after Wanjiazhai reservoir, and provide a basis for ice prevention and scheduling. [Methods] The processes of freezing-up and thawing of the river reach were analyzed based on the field observations from 1998 to 2015, including the dates and durations of both freezing-up and thawing and their relationships to the affecting factors like air temperature, flow rate, ice thickness, and number of days of frozen. The significant correlation coefficients were selected and BP neural network model was applied to predict the durations of the freezing-up, durations of thawing, and dates of thawing of the upstream reach respectively. [Results] The accuracy of the predicting results were up to 93% compared with the observed date. [Conclusion] Neural network model is suitable for forecasting freezing-up duration, breaking-up duration and breaking-date in upper reach of Wanjiazhai dam, and is more suitable for solving the problem of ice regime influenced by complex factors.

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张璐,张生,李超,熊运阜.万家寨水库上游的冰情特征分析及预报[J].水土保持通报,2017,37(1):196-200

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  • 收稿日期:2016-03-15
  • 最后修改日期:2016-06-10
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  • 在线发布日期: 2017-03-23
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