| 郭田丽,宋松柏,张特,王慧敏.基于两阶段粒子群优化算法的新型逐步分解集成径流预测模型[J].水利学报,2022,53(12):1456-1466 |
| 基于两阶段粒子群优化算法的新型逐步分解集成径流预测模型 |
| A new stepwise decomposition ensemble model based on two-stage particle swarm optimization algorithm for the runoff prediction |
| 投稿时间：2022-05-05 |
| DOI：10.13243/j.cnki.slxb.20220349 |
| 中文关键词: 径流预测 区间预测 分解集成模型 两阶段粒子群优化算法 变分模态分解 支持向量机 |
| 英文关键词: runoff prediction interval prediction decomposition ensemble model two-stage particle swarm optimization algorithm variable mode decomposition support vector machine |
| 基金项目:国家自然科学基金项目(52079110) |
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| 中文摘要: |
| 传统分解集成径流预测模型首先将整个径流序列分解成若干个子序列,再将这些子序列划分为训练期和验证期进行建模,错误地将验证期内预报因子数据视作已知数据处理,难以应用于实际径流预报工作中。并且,这类模型的预测结果仅为一个确定数值,难以描述由于径流序列随机性和波动性而导致的预测不确定性。为解决以上问题,本文结合变分模态分解方法、支持向量机模型和核密度估计方法,提出了一种可同时进行点预测和区间预测的新型逐步分解集成(VMD-SVM-KDE)模型,并提出了一种两阶段粒子群优化(TSCPSO)算法来优化模型参数。选用黄河流域月径流数据评估模型性能,研究结果表明:(1)VMD-SVM-KDE模型将单一SVM-KDE模型的确定系数(R2)和纳什效率系数(NSE)值由0.145~0.630提升至0.872~0.921,区间平均偏差(INAD)值由0.046~95.844降低至0.005~0.034,说明VMD-SVM-KDE模型显著改进了单一SVM-KDE模型的点预测和区间预测性能;(2)相较于一阶段PSO算法,TSCPSO优化算法将单一模型的R2和NSE值由0.145~0.480提升至0.309~0.630,INAD值由48.813~95.844降低至0.046~0.195,将分解集成模型的R2和NSE值由0.872~0.912提升至0.876~0.921,INAD值由0.007~0.034降低至0.005~0.014,说明TSCPSO优化算法可以克服SVM的过拟合问题,并能提高单一模型和分解集成模型的预测精度;(3)VMD-SVM-KDE-TSCPSO有效解决了传统分解集成预测模型存在的错误使用验证期内预报因子数据的问题,并在各站的R2和NSE值均约为0.9,INAD值的范围为0.005~0.014,具有更高的点预测和区间预测精度。文中模型可为优化径流预测模型和非平稳非线性水文序列预报提供新思路。 |
| 英文摘要: |
| The traditional decomposition ensemble runoff prediction model firstly decomposes the entire runoff series into several subseries,and then divides the subseries into training and validation periods for modeling,which wrongly treats the predictor data of validation period as known data and is difficult to be applied to actual runoff forecasting.Moreover,the prediction results of such models are only definite values,which is difficult to describe the prediction uncertainty caused by the randomness and volatility of runoff series.To solve the above problems,this study proposes a stepwise decomposition ensemble (VMD-SVM-KDE) model combining variable mode decomposition method,support vector machine model and kernel density estimation method,which performs both point prediction and interval prediction,and proposes a two-stage particle swarm optimization (TSCPSO) algorithm.The monthly runoff series of the Yellow River Basin is used to evaluate the model performance,and the study results show that:(1) the VMD-SVM-KDE model improves the coefficient of determination (R2) and Nash efficiency coefficient (NSE) values of the single SVM-KDE model from the range of 0.145 to 0.630 to the range of 0.872 to 0.921,and reduces the interval average deviation (INAD) values from the range of 0.046 to 95.844 to the range of 0.005 to 0.034,indicating that the VMD-SVM-KDE model significantly improves the point prediction and interval prediction performance of a single SVM-KDE model;(2) compared with the traditional one-stage PSO algorithm,the TSCPSO algorithm improves the R2 and NSE values of the single model from the range of 0.145 to 0.480 to the range of 0.309 to 0.630,and reduces the INAD value from the range of 48.813 to 95.844 to the range of 0.046 to 0.195,and also improves the R2 and NSE values of the decomposition ensemble model from the range of 0.872 to 0.912 to the range of 0.876 to 0.921,and reduces the INAD values from the range of 0.007 to 0.034 to the range of 0.005 to 0.014,indicating that the TSCPSO optimization algorithm overcomes the overfitting problem of support vector machine models and effectively improves the prediction accuracy of the single and decomposition ensemble models;(3) the VMD-SVM-KDE-TSCPSO model addressed the mistakes of traditional decomposition ensemble models that forecast factor data of validation period,and has higher accuracy of point prediction and interval prediction with R2 and NSE values of about 0.9 and the INAD values ranging from 0.005 to 0.014.The VMD-SVM-KDE-TSCPSO model can provide a basis for practical forecasting of non-stationary and non-linear hydrological series. |
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