Set pair weight Markov chain model based on sequence clustering method for dynamically predicting annual precipitation

HOU Zeyu, LU Wenxi, SONG Wenbo, LI Mengnan, CHEN Mo

Systems Engineering - Theory & Practice ›› 2016, Vol. 36 ›› Issue (4) : 1066-1071.

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中国科学院数学与系统科学研究院期刊网
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Systems Engineering - Theory & Practice ›› 2016, Vol. 36 ›› Issue (4) : 1066-1071. DOI: 10.12011/1000-6788(2016)04-1066-06

Set pair weight Markov chain model based on sequence clustering method for dynamically predicting annual precipitation

  • HOU Zeyu1,2, LU Wenxi1,2, SONG Wenbo1,2, LI Mengnan3, CHEN Mo1,2
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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.

Key words

precipitation prediction / sequence clustering / set pair analysis / Markov chain

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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. Systems Engineering - Theory & Practice, 2016, 36(4): 1066-1071 https://doi.org/10.12011/1000-6788(2016)04-1066-06

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Funding

China Geological Survey Project (1212011140027, 12120115032801);Jilin Foundation for Development of Science and Technology (20100215)
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