基于滑动灰色理论和加权马尔可夫链的降水预测

Yiqi Gui, Hwangkyu Choi
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引用次数: 2

摘要

降水预报已经成为我们生活中的一个重要问题。本文的目的是利用灰色理论和加权马尔可夫链预测扬州市的降水,以帮助人们在工业和生活中。首先对原始数据进行GM(1,1)的平均序列,建立滑动灰色加权马尔可夫模型;然后以1981- 2015年扬州市年降水量为例,应用该模型对2011 - 2015年的年降水量进行了预测。结果表明,本文提出的模型提高了预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Precipitation Based on Sliding Grey Theory and Weighted Markov Chain
Precipitation forecasting has become an important issue in our life. The purpose of this paper is to predict the precipitation in Yangzhou using Grey Theory and Weighted Markov Chain to help people in industry and life. Firstly the original data are averaged sequence for GM (1,1) and Sliding Grey Weighted-Markov model is established. Then we apply the proposed model to predict the annual precipitation from 2011 to 2015, based on annual precipitation in Yangzhou during 1981- 2015. The results show that this paper proposes the model improving the accuracy of prediction.
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