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

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