“分解-校正-集成”模式下基于深度信念网络模型的径流预测

钱玉霞; 陈伏龙; 何朝飞; 龙爱华; 孙怀卫; 吕廷波

doi:10.11988/ckyyb.20221671

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长江科学院院报 ›› 2024, Vol. 41 ›› Issue (5) : 35-44. DOI: 10.11988/ckyyb.20221671

水资源

“分解-校正-集成”模式下基于深度信念网络模型的径流预测

钱玉霞^1,2, 陈伏龙^1,2, 何朝飞^1,2, 龙爱华^1,3, 孙怀卫^1,4, 吕廷波^1,2

作者信息 +

Runoff Prediction Based on Deep Belief Network in Decomposition-Correction-Integration Mode

QIAN Yu-xia^1,2, CHEN Fu-long^1,2, HE Chao-fei^1,2, LONG Ai-hua^1,3, SUN Huai-wei^1,4, LÜ Ting-bo^1,2

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文章历史 +

摘要

精准的短期径流预测可为流域内水资源规划、防洪调度及抗旱减灾工作提供重要的科学依据。为减小模型的系统误差,提高径流预测精度,在“分解-集成”模式的基础上提出“分解-校正-集成”框架,构建EEMD-DBN-EnKF、VMD-DBN－EnKF模型。利用集合卡尔曼滤波数据同化算法对偏离实测径流过大的分量校正以降低分解子序列在预测中产生的系统误差,并与未修正的EEMD-DBN、VMD-DBN模型及单一DBN模型进行了对比分析。结果表明:基于模态分解的组合模型较单一模型RMSE减小了至少23%,NSE与R²增加了21%以上;基于径流分量校正的组合模型相较于模态分解的组合模型各评价系数有所提升,其中VMD-DBN－EnKF预测模型误差最小,效果最优,NSE与R²达到0.89以上,其次依次为EEMD-DBN-EnKF>VMD-DBN>EEMD-DBN。综上“分解-校正-集成”模式的预测框架在玛纳斯河流域具有良好的适用性,可为玛纳斯河径流短期预报提供技术支持。

Abstract

Accurate short-term runoff prediction can provide important scientific basis for water resources planning, flood control and drought relief in river basin. To mitigate systematic errors and enhance runoff prediction accuracy of models, we propose the decomposition-correction-integration framework based on the decomposition-integration model. Within this framework, we construct the EEMD-DBN-EnKF and VMD-DBN-EnKF models. Leveraging the Ensemble Kalman Filter data assimilation algorithm, we correct components deviating significantly from measured runoff to alleviate systematic errors introduced by the decomposition process in prediction. Comparative analysis is conducted against the unmodified EEMD-DBN, VMD-DBN, and single DBN models. Results demonstrate that the combination model based on modal decomposition reduces RMSE by a minimum of 23% compared to individual models, while NSE and R² increase by over 21%. Notably, the runoff component-corrected combined model exhibits improved evaluation metrics relative to the modal decomposition-based model. Among these models, the VMD-DBN-EnKF prediction model exhibits the least error and highest effectiveness, with NSE and R² exceeding 0.89, followed by EEMD-DBN-EnKF, VMD-DBN, and EEMD-DBN in descending order. In conclusion, the “decomposition-correction-integration” prediction framework demonstrates robust applicability in the Manas River Basin, offering valuable technical support for short-term runoff forecasts.

导出引用

钱玉霞, 陈伏龙, 何朝飞, 龙爱华, 孙怀卫, 吕廷波. “分解-校正-集成”模式下基于深度信念网络模型的径流预测[J]. 长江科学院院报. 2024, 41(5): 35-44 https://doi.org/10.11988/ckyyb.20221671

QIAN Yu-xia, CHEN Fu-long, HE Chao-fei, LONG Ai-hua, SUN Huai-wei, LÜ Ting-bo. Runoff Prediction Based on Deep Belief Network in Decomposition-Correction-Integration Mode[J]. Journal of Changjiang River Scientific Research Institute. 2024, 41(5): 35-44 https://doi.org/10.11988/ckyyb.20221671

中图分类号： P338.1

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