| 龙羊峡水库年入库径流的Markov预测模型 |
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| 引用本文： | 蓝永超,康尔泗,徐中民. 龙羊峡水库年入库径流的Markov预测模型[J]. 中国沙漠, 2000, 20(1): 95-97 |
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| 作者姓名： | 蓝永超 康尔泗 徐中民 |
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| 作者单位： | 中国科学院 寒区旱区环境与工程研究所, 甘肃 兰州 730000 |
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| 基金项目： | 国家"九·五"重点科技攻关项目96-912-01-02,96-912-03-03专题及国家自然科学基金重点项目(49731030)资助 |
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| 摘 要： | Markov预测技术是应用Markov链的相关预测的基本原理与方法来研究分析时间序列的变化规律,并预测其未来变化趋势的一种预报方法,适用于随机波动较大的预报问题。根据Markov链原理,提出了一个用于龙羊峡水库年平均入库径流预报的离散随机过程模型。实测资料的验证结果表明,这种模型计算精度较高,具有良好应用价值。
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| 关 键 词： | 离散随机过程 Markov链 径流预报 概率转移矩阵 |
| 文章编号： | 1000-694X(2000)01-0095-03 |
| 收稿时间： | 1999-05-11 |
| 修稿时间： | 1999-05-11 |
Markov Model for Forecasting the Inflow into the Longyangxia Reservoir |
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| LAN Yong-chao,KANG Er-si,XU Zhong-min. Markov Model for Forecasting the Inflow into the Longyangxia Reservoir[J]. ournal of Desert Research, 2000, 20(1): 95-97 |
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| Authors: | LAN Yong-chao KANG Er-si XU Zhong-min |
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| Affiliation: | Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China |
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| Abstract: | The catchment above Tangnag station is the principal areas for runoff formation in the upper reaches of the Yellow River. For example, the water flows from the Tangnag Station account for 95% of the inflows into the Longyangxia Reservoir,the largest reservoir on the upper Yellow River. However, runoff in the upper Yellow River above the Tangnag has been decreasing recently due to the persisting drought in the basin, which not only greatly influences the economy and people's standards of living in the upper reaches, but also curbs the economic development and ecological environment imporvement within the Yellow River basin. As a result, the accurate prediction for the variation of the runoff at the Tangnag Station is indispensable for the efficient and logical exploitation of the water resources at the catchment scale, as well as determining the amount of water transported from other basin. However, sophisticated methods are not available at present to determine the variability of water flows, due to complexity of their intrinsic evolutions, and close and complicated relationships to climatic changes. In addition, precision of runoff prediction is greatly influenced by the difficulty in long term weather forecasting. To solve the above problems, a dispersal stochastic process model based on the Markov Chain was presented for forecasting the inflow into the Longyangxia Reservoir in this paper. This forecasting method is mainly applicable to the objects that are of biggish stochastic vibration. The results show that the calculated values can commendably tally with the measured values. Therefore, Morkov forecast model is of obvious advantage. |
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| Keywords: | dispersal stochastic process Markov Chain runoff forecasting probability transfer matrix |
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