Computer Science ›› 2017, Vol. 44 ›› Issue (10): 222-227.doi: 10.11896/j.issn.1002-137X.2017.10.040

Previous Articles     Next Articles

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

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] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .