首页|基于混合孪生支持向量机的径流区间预测

基于混合孪生支持向量机的径流区间预测

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
径流具有非线性和随机性特征,单一点预测模型难以精确刻画和描述径流演化过程.为此,提出了一种可有效量化径流波动范围的智能区间预测方法.首先采用自适应噪声完备集合经验模态分解将非线性径流序列划分为若干子序列,并采用样本熵方法重构得到修正序列;其次以孪生支持向量机为基础,分别对复杂度较高的子序列构建区间预测模型、复杂度较低的子序列建立点预测模型,同时采用鲸鱼优化方法寻求满意的模型参数组合;最后将各子模型的预测结果叠加得到最终的预测区间.结果表明:所提方法具有良好的稳健性和可靠性,在点预测、区间预测等不同场景、不同预见期的性能指标均优于对比模型;如预见期为3d时,对于黄河流域唐乃亥水文站,所得预测区间具有较高的可靠度与清晰度,其预测区间覆盖率 PICP 值为98.30%,预测区间平均宽度PINAW值为0.0792,可靠度、清晰度分别平均提高了 9.47%和 32.66%.研究成果可为智能化径流预测提供行之有效的方法.
Hybrid twin support vector regression for runoff interval prediction
Due to the nonlinear and stochastic characteristics of runoff,a single-point prediction model is inadequate in accu-rately capturing and describing the runoff evolution process.To address this issue,we introduced an intelligent interval prediction method that effectively quantifies the range of runoff fluctuations.Firstly,the complete ensemble empirical mode decomposition with adaptive noise was utilized to partition the nonlinear runoff sequence into multiple subseries,and the sample entropy method was employed to reconstruct the modified subseries.Secondly,employing the twin support vector regression as a foundation,inter-val prediction models were constructed for the more complex subseries,while point prediction models were established for the rela-tively simpler ones.Simultaneously,the whale optimization algorithm was employed to seek satisfactory combinations of model pa-rameters.Finally,the prediction results from each submodel were aggregated to derive the ultimate prediction interval.Application results demonstrated that the proposed method exhibits remarkable stability and dependability,surpassing comparative models in various scenarios and prediction periods,including both point prediction and interval prediction.When the forecast period was 3 d,the anticipated interval derived for the Tangnaihai hydrological station in the Yellow River Basin had good reliability and clari-ty,with a Prediction Interval Coverage Probability(PICP)value of 98.30%and a Prediction Interval Normalized Average Width(PINAW)value of 0.0792.Average increases in reliability and clarity were9.47%and32.66%,respectively.The results can provide effective models for intelligent runoff interval prediction.

runoff predictiontwin support vector regressioncomplete ensemble empirical mode decomposition with adaptive noisewhale optimization algorithmYellow River Basin

冯仲恺、付新月、纪国良、刘亚新、牛文静、黄海燕、杨涛

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河海大学 水文水资源学院,江苏 南京 210098

中国长江三峡集团有限公司,湖北 宜昌 443100

中国长江电力股份有限公司,湖北 宜昌 443133

长江水利委员会 水文局,湖北 武汉 430010

云南水利水电职业学院,云南 昆明 650051

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径流预测 孪生支持向量机 自适应噪声完备集合经验模态分解 鲸鱼优化方法 黄河流域

国家重点研发计划湖北省自然科学基金中国长江三峡集团有限公司科研项目云南水利水电职业学院项目

2022YFC32023002023AFD02307992482023SZYKL005

2024

10.16232/j.cnki.1001-4179.2024.04.014

人民长江
水利部长江水利委员会

人民长江

CSTPCD北大核心
影响因子：0.451
ISSN：1001-4179
年,卷(期)：2024.55(4)
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