Application of dynamic Bayesian networks in hydrologic forecast

doi:10.3778/j.issn.1002-8331.2010.06.067

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (6): 231-234.DOI: 10.3778/j.issn.1002-8331.2010.06.067

• 工程与应用 • Previous Articles Next Articles

Application of dynamic Bayesian networks in hydrologic forecast

LI Wei-qian^1，2，XIE Jian-cang¹，ZHANG Yong-jin³，XUE Bao-ju¹，ZHANG Li⁴

1.Institute of Water Resource and Hydro-electric Engineering，Xi’an University of Technology，Xi’an 710048，China
2.Institute of Mathematics and Information，Gansu Lianhe University，Lanzhou 730000，China
3.Institute of Business Administration，Xi’an University of Technology，Xi’an 710048，China
4.Xi’an University of Finance and Economics，Xi’an 710061，China

Received:2008-08-29 Revised:2008-11-13 Online:2010-02-21 Published:2010-02-21
Contact: LI Wei-qian

动态贝叶斯网络在水文预报中的应用

李维乾^1，2，解建仓¹，张永进³，薛保菊¹，张丽⁴

1.西安理工大学水利水电学院，西安 710048
2.甘肃联合大学数学与信息学院，兰州 730000
3.西安理工大学工商管理学院，西安 710048
4.西安财经学院，西安710061

通讯作者: 李维乾

Abstract

Abstract: Bayesian network is one of the most efficient models in the uncertain knowledge and reasoning field.A rainfall-runoff prediction model based on dynamic Bayesian network is put forward in this paper.The network model is based on knowledge of the field experts and the causes of rainfall-runoff，they can produce the probability of the flow rate by calculating the conditional probability among variables on the basis of historical rainfall and runoff data.Finally through the simulation of historical data about Wei He river basin from Xianyang gauge station to Lintong gauge station，the model and the results are analyzed.

Key words: Bayesian network, dynamic Bayesian network, hydrologic forecast, data mining, higher-order Markov

摘要： 贝叶斯网络是目前人工智能中不确定知识与推理中最有效的理论模型之一。提出一种基于动态贝叶斯网络模型理论的水文预报方法。在综合考虑降雨径流成因的基础上，利用领域专家知识构建网络模型，在已有降雨、流量数据的基础上通过计算变量间的条件概率来计算流量发生的可能性。最后，通过渭河流域咸阳至临潼段历时数据进行仿真实验，对仿真结果和该模型进行了分析。

关键词: 贝叶斯网络, 动态贝叶斯网络, 水文预报, 数据挖掘, 高阶马尔科夫链

CLC Number:

TP391

LI Wei-qian^1，2，XIE Jian-cang¹，ZHANG Yong-jin³，XUE Bao-ju¹，ZHANG Li⁴. Application of dynamic Bayesian networks in hydrologic forecast[J]. Computer Engineering and Applications, 2010, 46(6): 231-234.

李维乾^1，2，解建仓¹，张永进³，薛保菊¹，张丽⁴. 动态贝叶斯网络在水文预报中的应用[J]. 计算机工程与应用, 2010, 46(6): 231-234.

[1]	ZONG Xiaoping, TAO Zeze. Knowledge Tracing Model Based on Mastery Speed [J]. Computer Engineering and Applications, 2021, 57(6): 117-123.
[2]	GAO Tianyu, WANG Qingrong, YANG Lei. Data Mining Model Based on Attribute Dependability Enhancement of Rough Set [J]. Computer Engineering and Applications, 2021, 57(3): 87-93.
[3]	MA Yang, ZHAO Xujun. Multi-source Outlier Detection Algorithm Based on Relevant Subspace [J]. Computer Engineering and Applications, 2021, 57(17): 88-95.
[4]	ZHANG Nianpeng, WU Xu, ZHU Qiang. Entropy-Based Oversampling Framework [J]. Computer Engineering and Applications, 2021, 57(13): 96-101.
[5]	ZHANG Bowen, LIU Zhi, SANG Guoming. Anomaly Detection Algorithm Based on Kernel Density Fluctuation [J]. Computer Engineering and Applications, 2021, 57(12): 132-136.
[6]	RAO Jiawang, MA Ronghua. Improved Kernel Density Estimator Based Spatial Point Density Algorithm [J]. Computer Engineering and Applications, 2021, 57(11): 260-265.
[7]	GUO Xuekun, QIN Yuanhui, DU Liang, LUO Yongliang, WANG Hongan. Flight Deck Aviation Support Job Detection Based on Multi-entity Bayesian Networks [J]. Computer Engineering and Applications, 2021, 57(1): 242-253.
[8]	CHEN Haiyang, LIU Xiqing, HUAN Xiaomin. One-step Prediction SVDDBN Missing Data Interpolation Algorithm [J]. Computer Engineering and Applications, 2020, 56(7): 81-87.
[9]	WANG Jie, CHEN Zhigang, LIU Jialing, CHENG Hongbing. Privacy Behavior Mining Technology for Cloud Computing Based on Clustering [J]. Computer Engineering and Applications, 2020, 56(5): 80-84.
[10]	WANG Zilong, LI Jin, SONG Yafei. Improved K-means Algorithm Based on Distance and Weight [J]. Computer Engineering and Applications, 2020, 56(23): 87-94.
[11]	JI Wenlu, WANG Hailong, SU Guibin, LIU Lin. Review of Recommendation Methods Based on Association Rules Algorithm [J]. Computer Engineering and Applications, 2020, 56(22): 33-41.
[12]	YI Junyan, WU Boya, YONG Qiaoling. Research on Clustering Algorithm of Elastic Net with Weighted Characteristics [J]. Computer Engineering and Applications, 2020, 56(22): 55-65.
[13]	LIU Wenfen, MU Xiaodong, HUANG Yuehua. Anomaly Detection Method Based on Multi-resolution Grid [J]. Computer Engineering and Applications, 2020, 56(17): 78-85.
[14]	ZHENG Wei, HOU Hongxu, BAN Zhijie. Expert Finding Method Using Baysian Network on Query Semantic Extension [J]. Computer Engineering and Applications, 2020, 56(13): 194-198.
[15]	MENG Haidong1，2, SUN Xinjun2, SONG Yuchen1. Improved LOF Algorithm Based on Data Field [J]. Computer Engineering and Applications, 2019, 55(3): 154-158.

Application of dynamic Bayesian networks in hydrologic forecast

动态贝叶斯网络在水文预报中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics