• (1)2008-2021年连续14年获得中国科技核心期刊
  • (2)2021年被中国科学院文献情报中心中国科学引文数据库CSCD(核心库)收录(2021-2022)
  • (3)热烈庆祝《水资源与水工程学报》入编北京大学图书馆《中文核心期刊要目总览》2020年版
王树威, 李建林, 崔延华, 高培强, 赵帅鹏.混沌理论与BPNN耦合的径流中长期预测模型水资源与水工程学报[J].,2021,32(3):73-79
混沌理论与BPNN耦合的径流中长期预测模型
Medium and long term runoff prediction model coupled with chaos theory and BPNN
  
DOI:10.11705/j.issn.1672-643X.2021.03.11
中文关键词:  混沌  BPNN  等维递补  径流中长期预测  黑河出山径流
英文关键词:chaos  back propagation neural network (BPNN)  equal dimension replenishment  medium and long term runoff prediction  runoff of the upper reaches of Heihe River
基金项目:国家自然科学基金项目(41672240、41573095); 河南省自然科学基金项目(182300410155)
作者单位
王树威1, 李建林1,2, 崔延华3, 高培强1, 赵帅鹏1 (1.河南理工大学 资源环境学院河南 焦作 454000 2.煤炭安全生产与清洁高效利用省部共建协同创新中心河南 焦作 454000 3.广西大藤峡水利枢纽开发有限责任公司 广西 南宁 530000) 
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中文摘要:
      受诸多因素的影响,径流时间序列具有非线性和混沌特征。单一的BPNN模型可以进行径流的中长期预测,但存在对径流影响因素量化不够的缺点;单一的混沌模型可以量化径流的影响因素,但只能实现短期预测。为此建立了混沌理论与BPNN耦合的径流中长期预测模型。针对黑河上游莺落峡水文站1944-2017年的径流序列,利用混沌理论计算了径流序列的延迟时间τ、嵌入维数m和最大Lyapunov指数λmax,并进行了径流序列的相空间重构,以此确定BPNN的输入层神经元个数、取值和预测的周期时长;利用BPNN对1944年1月-2012年12月的径流量数据进行训练,建立了混沌-BPNN和混沌-BPNN等维递补两种预测模型;以2013年1月-2017年12月(5 a)的径流量进行模型验证。结果表明:混沌-BPNN等维递补模型的预测精度达到了91.84%,预测效果较好。混沌理论与BPNN耦合的径流预测模型将两种方法的优势互补,尤其是混沌-BPNN等维递补模型,在补充新信息的同时剔除因系统发展而使特征意义降低的老数据,减小了BPNN训练的时间跨度,提高了预测精度,为径流的中长期预测提供了新的有效方法。
英文摘要:
      Under the influence of multiple factors, runoff time series shows nonlinear and chaotic characteristics. Back propagation neural network (BPNN) model alone can be used to the medium and long term prediction of the runoff, but it has the disadvantage of insufficient quantification of runoff influencing factors. Single chaos theory model can quantify the influencing factors of runoff, but it is only applicable to the short term prediction. In view of this situation, a medium and long term runoff prediction model coupled with chaos theory and BPNN was established. Time delay τ, embedding dimension m and maximum Lyapunov exponent λmax of the runoff series of Yingluoxia Hydrological Station in the upper reaches of Heihe River from 1944 to 2017 were calculated by chaos theory, and the phase space reconstruction of the runoff series was carried out to determine the number and values of neurons in the input layer of BPNN, and the time span of the prediction. Then BPNN was used to train the runoff data from January 1944 to December 2012, based on which two prediction models of chaos-BPNN and chaos-BPNN equal dimension replenishment were established. The runoff data from January 2013 to December 2017 were used to verify the models. The result showed that the prediction accuracy of the chaos-BPNN equal dimension replenishment model reached 91.84%, with a satisfactory prediction effect. The runoff prediction models coupled with chaos theory and BPNN combine the advantages of the two methods, especially the chaos-BPNN equal dimension replenishment model. It can not only supplement new information but also eliminate the old data which has lost its purpose due to the development of the system, thus reducing the time span of BPNN training and improving the prediction accuracy. This model is a new effective approach to the prediction of medium and long term runoff.
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