| 基于有序样品聚类的集对权马尔可夫链年降水量预测模型 |
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| 引用本文： | 侯泽宇,卢文喜,宋文博,李孟南,陈末.基于有序样品聚类的集对权马尔可夫链年降水量预测模型[J].系统工程理论与实践,2016,36(4):1066-1071. |
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| 作者姓名： | 侯泽宇 卢文喜 宋文博 李孟南 陈末 |
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| 作者单位： | 1. 吉林大学地下水资源与环境教育部重点实验室, 长春 130026;2. 吉林大学环境与资源学院, 长春 130026;3. 吉林省水利水电勘测设计研究院, 长春 130021 |
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| 基金项目： | 中国地质调查局项目(1212011140027,12120115032801);吉林科学技术发展基金(20100215) |
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| 摘 要： | 本文尝试将有序样品聚类、集对分析和马尔可夫链三种方法相结合,对传统的加权马尔可夫链预测方法进行了多方面改进,建立了基于有序样品聚类的集对权马尔可夫链年降水量预测模型,并将其应用于吉林省白城地区白城站2008-2010年年降水量的预测.将预测结果与实测值进行对比分析,可以发现:与传统方法相比,改进方法可以使降水量等级区间的划分更加合理,并增加了预测概率的集中程度,有效提高了预测精度.实测值均位于预测区间内,表明该方法具有较高实际应用价值.作为降水量预测模型的改进尝试,其预测效果令人满意.
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| 关 键 词： | 降水量预测 有序样品聚类 集对分析 马尔可夫链 |
| 收稿时间： | 2014-10-08 |
Set pair weight Markov chain model based on sequence clustering method for dynamically predicting annual precipitation |
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| HOU Zeyu,LU Wenxi,SONG Wenbo,LI Mengnan,CHEN Mo.Set pair weight Markov chain model based on sequence clustering method for dynamically predicting annual precipitation[J].Systems Engineering —Theory & Practice,2016,36(4):1066-1071. |
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| Authors: | HOU Zeyu LU Wenxi SONG Wenbo LI Mengnan CHEN Mo |
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| Institution: | 1. MOE Key Laboratory of Groundwater Resources and Environment, Jilin University, Changchun 130026, China;2. College of Environment and Resources, Jilin University, Changchun 130026, China;3. Jilin Provincial Investigation and Design Institute of Water Resources and Hydropower, Changchun 130021, China |
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| Abstract: | A set pair weight Markov chain model based on sequence clustering method for predicting annual precipitation was established in this paper and applied to forecast the precipitation of Baicheng station (Jilin Province) during 2008-2010. It was an improvement of the traditional method by combining sequence clustering method, set pair analysis and Markov chain. Research results show that the improved method make the partition of precipitation grade interval more reasonable. In addition, it can effectively improve the concentration of prediction probability and the prediction accuracy. The measured values all lie in the prediction interval. In conclusion, the method is with high practical application value. As an attempt of the improvement of precipitation prediction model, its prediction effect is satisfactory. |
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| Keywords: | precipitation prediction sequence clustering set pair analysis Markov chain |
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