计算机科学 ›› 2017, Vol. 44 ›› Issue (10): 222-227.doi: 10.11896/j.issn.1002-137X.2017.10.040

• 人工智能 • 上一篇    下一篇

多分支的降水量概率预测模型研究

余霖,吕鑫,周思琪,刘璇   

  1. 河海大学计算机与信息学院 南京211100,河海大学计算机与信息学院 南京211100,河海大学计算机与信息学院 南京211100,河海大学计算机与信息学院 南京211100
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受NSFC-广东联合基金重点项目(U1301252),水利部公益性行业科研专项重点项目(201501007),国家自然科学基金面上项目(61272543),国家科技支撑计划项目(HNKJ13-H17-04)资助

Research of Multi-branch Precipitation Probability Forecasting Model

YU Lin, LV Xin, ZHOU Si-qi and LIU Xuan   

  • Online:2018-12-01 Published:2018-12-01

摘要: 降水量大小对水资源调度决策、防汛防旱预警等方面有着决定性作用。目前已有大量降水量预测模型被提出,但其由于缺乏对降水过程非线性性态的考虑,因此预测准确度不高。另外,单独的预测值难以对决策判断形成有效支持,使预测结果的应用性不好。针对上述问题,基于降水量的平稳性及周期性,构建了同比分支及环比分支预测模型,进而提出了一种多分支的降水量概率预测模型MBPPFM。该模型采用十字交叉选择算法,精细化筛选同比、环比分支预测结果,提高了预测准确性,并能避免异常预测。同时,预测结果包括区间概率和结果置信度,能有效支持决策形成。

关键词: 降水量预测,ARMA模型,BP网络,Markov预测,关联规则,十字交叉模型

Abstract: The size of precipitation plays a decisive role in aspects of water dispatching decision,early warning of flood and drought control,etc.Currently,many precipitation forecasting models have already been put forward.However,due to lack of the nonlinear characteristic of precipitation process consideration,the forecasting accuracy is not high.In addition,it is difficult to use a single forecast value to effectively support the judgment,leading to the fact of lower applicability results.Aimed at the above-mentioned problems,forecasting models of year-on-year branch and month-on-month branch were constructed based on the stationarity and periodicity of precipitation,and then a multi-branch precipitation probability forecasting model (MBPPFM) was proposed.The cross selection algorithm was used in the model to well screen the forecasting results from year-on-year branch and month-on-month branch.Finally,the forecasting accuracy is improved and abnormal forecasting can be avoided.At the same time,probability and confidence values are included in the forecasting results to effectively support decision making.

Key words: Precipitation forecasting,ARMA model,BP network,Markov forecasting,Association rules,Cross model

[1] BI Z L,ZHANG Z Y,ZHU X G,et al.Precipitation Predicting Based on Improved RBF Neural Network and Markov Model[J].Water Saving Irrigation,2010(11):1-3.(in Chinese) 闭祖良,张展羽,朱新国,等.基于RBF神经网络马尔可夫模型的降水量预测[J].节水灌溉,2010(11):1-3.
[2] ZHANG A.Henan Zhaokou Irrigation Management System Basedon WebGIS[D].Zhengzhou:Zhengzhou University,2012.(in Chinese) 张昴.基于WebGIS的河南省赵口灌区管理信息系统[D].郑州:郑州大学,2012.
[3] TIAN J W,SHANG S H,SUN Y L,et al.Stochastic characte-ristics of reference evapotranspiration and precipitation of Xiaohe Irrigation Areas,Shanxi Province[J].Transactions of the CSAE,2005,21(10):26-30.(in Chinese) 田俊武,尚松浩,孙艳丽,等.山西潇河灌区参考作物腾发量和降水的随机特性[J].农业工程学报,2005,21(10):26-30.
[4] LI X G,LIU X Z.Study on ARIMA stochastic model for precipitation in Yantai Region[J].Journal of Water Resources and Water Engineering,2006,17(2):505-510.(in Chinese) 李希国,刘贤赵.烟台地区降水量的ARIMA随机模型研究[J].水利科技与经济,2006,17(2):505-510.
[5] WANG S F,ZHANG Z Y,DUAN A W,et al.Time Sequence Characteristic Analysis of Precipitation in North Area of Henan Province[J].China Rural Water and Hydropower,2008(3):13-16.(in Chinese) 王声锋,张展羽,段爱旺,等.豫北地区降水的时问序列特性分新[J].中国农村水利水电,2008(3):13-16.
[6] YANG L L,LU W X.The Application of Time Series Analysis in Precipitation Forecast in Wuyuan County[C]∥2011 International Symposium on Water Resource and Environmental Protection (ISWREP).IEEE,2011:3063-3065.
[7] HE H,JIN L,QIN Z N,et al.Downscaling Forecast of Monthly Precipitation over Guangxi Based on BP Neural Network Model[J].Journal of Tropical Meteorology,2007,13(1):97-100.
[8] LIU L,YE W.Precipitation prediction of time series model basedon BP artificial neural network[J].Journal of Water Resources and Water Engineering,2010,21(5):156-159.(in Chinese) 刘莉,叶文.基于BP神经网络时间序列模型的降水量预测[J].水资源与水工程学报,2010,21(5):156-159.
[9] ZHANG J X,WANG P,ZHANG L,et al.Application of artificial neural network in short term precipitation forecast[J].Technology Wind,2016(17):123-124.(in Chinese) 张继学,王鹏,张琳,等.人工神经网络在短期降水预测方面的应用研究[J].科技风,2016(17):123-124.
[10] CHI Z X,BAI H.The Study on Short-term Climatic ForecastWay in Southeast Guizhou[J].Desert and Oasis Meteorology,2005,8(6):20-21.(in Chinese) 池再香,白慧.黔东南地区短期气候预测方法研究[J].沙漠与绿洲气象,2005,8(6):20-21.
[11] LU Z Y,YANG L,ZHAO Z C,et al.A Field Feature Extraction Method of Sand-dust Storm Ensemble Forecast System Based on ANN[J].Computer Simulation,2007,24(6):341-344.(in Chinese) 路志英,杨乐,赵智超,等.沙尘暴综合预报系统中场特征提取方法的研究[J].计算机仿真,2007,24(6):341-344.
[12] HUANG J P.Research on PID Controller Based on BP Neural Network[J].Computer Simulation,2010,27(7):167-170.(in Chinese) 黄剑平.基于BP神经网络的PID控制研究[J].计算机仿真,2010,27(7):167-170.
[13] CAO Y Q,HOU W P.Application Research of Non-linear Theo-ry in Hydrology and its Prospect[J].Water Power,2005,31(4):14-17.(in Chinese) 曹永强,侯文萍.非线性理论在水文学中的应用研究及展望[J].水力发电,2005,31(4):14-17.
[14] QING H.Exact Distribution Theory of Runs[J].Chinese Journal of Applied Probability and Statisties,1999,15(2):199-212.
[15] 李世华.基于马尔可夫模型的澜沧县降雨状态及降水量预测应用[C]∥云南省水利学会2015年度学术年会论文集.昆明:云南省水利学会,2015:688-692.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75, 88 .
[2] 夏庆勋,庄毅. 一种基于局部性原理的远程验证机制[J]. 计算机科学, 2018, 45(4): 148 -151, 162 .
[3] 厉柏伸,李领治,孙涌,朱艳琴. 基于伪梯度提升决策树的内网防御算法[J]. 计算机科学, 2018, 45(4): 157 -162 .
[4] 王欢,张云峰,张艳. 一种基于CFDs规则的修复序列快速判定方法[J]. 计算机科学, 2018, 45(3): 311 -316 .
[5] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[6] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[7] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[8] 刘琴. 计算机取证过程中基于约束的数据质量问题研究[J]. 计算机科学, 2018, 45(4): 169 -172 .
[9] 钟菲,杨斌. 基于主成分分析网络的车牌检测方法[J]. 计算机科学, 2018, 45(3): 268 -273 .
[10] 史雯隽,武继刚,罗裕春. 针对移动云计算任务迁移的快速高效调度算法[J]. 计算机科学, 2018, 45(4): 94 -99, 116 .