干旱区生态环境知识资源中心(Arid): 河流年径流量的GM(1,2)-Markov中长期预测模型

Arid

	河流年径流量的GM(1,2)-Markov中长期预测模型
其他题名	AGM(1,2)-Markov model for mid-long term annual river runoff prediction
	李建林 1; 李志强 2; 王心义 1; 郑继东 1; 昝明军 2
来源期刊	干旱区地理
ISSN	1000-6060
出版年	2016
卷号	39 期号:2 页码:240-245
中文摘要	径流过程具有随机和灰色特征。基于此,将Markov预测与灰色GM(1,2)预测相结合,提出了GM(1,2)-Markov中长期河流年径流量预测模型。通过对黑河正义峡、莺落峡水文站65 a(1949-2014年)的年径流量资料分析,将莺落峡年径流量作为GM(1,2)预测的相关因素数据列,以1990-2009年的数据建立正义峡年径流量GM(1,2)-Markov模型,以2010-2014年的年径流量进行模型验证。结果表明:莺落峡、正义峡年径流量具有较强的相关性;建立的正义峡年径流量预测模型精度为83.65%;预测未来5 a的径流量,预测精度达到了97.12%;GM(1,2)-Markov、GM(1,1)和GM(1,2)模型的模型精度都符合建模的要求,但GM(1,2)-Markov模型的预测精度明显高于GM(1,1)模型和GM(1,2)模型。GM(1,2)-Markov模型为河流径流量的中长期预测提供了一种新方法。
英文摘要	In arid areas,mid-long term runoff forecasting is very important for water resources planning and management. Because river runoff series has both randomness and gray characteristics,in China,the gray theory was used for forecasting runoff firstly in the late 1980s,and Markov prediction was used for forecasting runoff beginning this century. For long time series and large random fluctuations series,the prediction effect of gray model was poor and had lower accuracy. Meanwhile,Markov prediction model need data of random and long time series. Both forecasting methods are highly complementary. Therefore,in order to predict the mid-long term annual runoff, some studies had constructed GM(1,1)-Markov prediction model by combining the gray system theory with Markov prediction. Compared with GM(1,1)model,GM(1,2)model introduced a reference series,which have a strong association with the main series. So GM(1,2)model can improve the prediction accuracy of volatility series. In this paper,a GM(1,2)-Markov prediction model was proposed. The paper constructed a GM(1,2)-Markov prediction model for Zhengyixia Station based on data of the Heihe River,Gansu Province,China. Firstly,the annual runoff correlation between Zhengyixia Station and Yingluoxia Station of the Heihe River during the period 1949-2014 was analyzed. The annual runoff between Zhengyixia Station and Yingluoxia Station has a strong correlation. So the data of Yingluoxia Station acted as relevant factor data columns,and the data of Zhengyixia Station acted as controlling factors data columns to construct GM(1,2)prediction model. Afterwards,the GM(1,2)-Markov prediction model was established based on the annual runoff data from 1990 to 2009,and this model was verified with the annual runoff data from 2010 to 2014. In order to verify the merits of the GM(1,2)-Markov model, using the same data to establish Zhengyixias annual runoff both GM(1,1)prediction model,and GM(1,2)prediction model. The GM(1,2)-Markov model was then compared with two other methods. To GM(1,1)model,GM(1,2)model and GM(1,2)-Markov model of Zhengyixias annual runoff prediction,the model accuracy of 83.83 %,82.62 % and 83.65 % were obtained respectively. Corresponding the next 5 years(2010-2014)prediction accuracy of 82.44 %,95.75 % and 97.12 % were obtained respectively. It means that the model accuracy of GM(1, 2)-Markov model,GM(1,1)model and GM(1,2)model are meet with the modeling requirements. However,prediction accuracy of GM(1,2)-Markov model are higher than that of GM(1,1)model and GM(1,2)model. The results show that the GM(1,2)-Markov model has the highest prediction accuracy compared to other models. This paper firstly proposed the GM(1,2)-Markov model for mid-long term runoff forecasting in the relating research fields. The GM(1,2)-Markov prediction model not only provides a new scientific method for annual river runoff of the mid- and long-term prediction,but also extends the applications range of Markov prediction and grey theory.
中文关键词	年径流量 ; 中长期预报 ; GM(1,2)模型 ; Markov预测 ; 正义峡
英文关键词	annual runoff mid-long term prediction GM(1,2)model Markov prediction Zhengyixia Station
语种	中文
国家	中国
收录类别	CSCD
WOS类目	ENGINEERING MULTIDISCIPLINARY
WOS研究方向	Engineering
CSCD记录号	CSCD:5684791
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/234234
作者单位	1.河南理工大学资源环境学院, 中原经济区煤层(页岩)气河南省协同创新中心, 焦作, 河南 454000, 中国; 2.河南理工大学资源环境学院, 焦作, 河南 454000, 中国
推荐引用方式 GB/T 7714	李建林,李志强,王心义,等. 河流年径流量的GM(1,2)-Markov中长期预测模型[J],2016,39(2):240-245.
APA	李建林,李志强,王心义,郑继东,&昝明军.(2016).河流年径流量的GM(1,2)-Markov中长期预测模型.干旱区地理,39(2),240-245.
MLA	李建林,et al."河流年径流量的GM(1,2)-Markov中长期预测模型".干旱区地理 39.2(2016):240-245.