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径流序列相空间重构的水文学含义及应用

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
为确定径流序列相空间重构后的水文学含义并提高径流中长期预测精度,基于混沌理论进行径流序列相空间重构,并对径流影响因素与重构后相空间列向量进行相关性分析.在此基础上建立了混沌理论与人工神经网络耦合(Chaos-BPNN)的径流预测模型,并应用于黑河上游莺落峡水文站和正义峡水文站.结果表明:径流序列重构后相空间列向量具有明确的水文学含义;Chaos-BPNN径流预测模型仅需径流序列数据就可进行建模和预测,规避了径流预测过程中主控因素难以确定和不易量化的问题;黑河上游降水量、输沙量、水位和气温分别与重构后相空间的第1、3、6、7列具有较高的相关性,风速与任何一列都不相关,推测雪线高程、植被覆盖率以及土地利用类型等因素与第2、4、5列存在相关性;构建的Chaos-BPNN径流预测模型在黑河上游莺落峡水文站和正义峡水文站的径流预测精度均在86％以上.
Hydrological meaning and application of phase space reconstruction of runoff series
To determine the hydrological meaning of the reconstructed phase space of the runoff series and improve the accuracy of mid-to long-term runoff prediction,the phase space of the runoff series was reconstructed based on chaos theory,and correlation analysis was conducted between the influencing factors of runoff and the reconstructed phase space column vectors.On this basis,a runoff prediction model coupled with Chaos theory and artificial neural network(Chaos-BPNN)was established,and applied to the Yingluoxia and Zhengyixia hydrological stations in the upper reaches of the Heihe River.The results indicate that the reconstructed phase space column vectors of the runoff series have clear hydrological meaning.The Chaos-BPNN runoff prediction model only requires runoff sequence data for modeling and prediction,avoiding the problems of difficult determination and quantification of main control factors in the runoff prediction process.The precipitation,sediment transport,water level,and temperature in the upper reaches of the Heihe River are highly correlated with the reconstructed phase space columns 1,3,6,and 7,respectively.Wind speed is not correlated with any column,and it is speculated that factors such as snow line elevation,vegetation coverage,and land use type are correlated with columns 2,4,and 5.The Chaos-BPNN runoff prediction model constructed has a runoff prediction accuracy of over 86％at the Yingluoxia and Zhengyixia hydrological stations in the upper reaches of the Heihe River.

runoff seriesphase space reconstructionchaotic characteristicsinfluencing factors of runoffChaos-BPNN runoff prediction model

李建林、贺奇、王树威、王心义、张杰

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河南理工大学资源环境学院,河南焦作 454000

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

中化地质矿山总局浙江地质勘查院,浙江杭州 310002

径流序列 相空间重构 混沌特征 径流影响因素 Chaos-BPNN径流预测模型

国家自然科学基金国家自然科学基金河南省高等学校重点科研项目

419722544216202122A170009

2024

10.3880/j.issn.1004-6933.2024.03.011

水资源保护
河海大学 中国水利学会环境水利研究会

水资源保护

CSTPCD北大核心EI
影响因子：0.827
ISSN：1004-6933
年,卷(期)：2024.40(3)
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