基于集合卡尔曼滤波与长短期记忆神经网络的联合校正方法及其在径流模拟中的应用

吕泳霖; 刘新波; 王鹏翔; 翟少军; 曹辉; 齐泓玮

doi:10.14188/j.1671-8844.2025.0095

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基于集合卡尔曼滤波与长短期记忆神经网络的联合校正方法及其在径流模拟中的应用

更新时间：2026-10-15

- 基于集合卡尔曼滤波与长短期记忆神经网络的联合校正方法及其在径流模拟中的应用
- Application of combined correction method based on Ensemble Kalman Filter and LSTM neural network in hydrological modelling
- 武汉大学学报（工学版） 2026年页码：1-12
- 作者机构：
  
  1.中国长江电力股份有限公司，湖北宜昌 443002
  2.武汉大学水利水电学院，湖北武汉 430072
- 作者简介：
  
  吕泳霖（2001-），女，硕士研究生，主要从事机器学习与水文过程模拟相关研究，E-mail: 2024282060082@whu.edu.cn
  刘新波（1990-），男，高级工程师，主要从事水利遥感与定量遥感相关研究，E-mail: liu_xinbo@ctg.com.cn
- 基金信息：
  
  中国长江电力股份有限公司资助项目(Z242302030)
- DOI：10.14188/j.1671-8844.2025.0095
  中图分类号： P333
- 网络首发：2026-10-15，
- 稿件说明：
移动端阅览
吕泳霖, 刘新波, 王鹏翔, 等. 基于集合卡尔曼滤波与长短期记忆神经网络的联合校正方法及其在径流模拟中的应用[J/OL]. 武汉大学学报（工学版）, 2026,1-12.

LV Yonglin, LIU Xinbo, WANG Pengxiang, et al. Application of combined correction method based on Ensemble Kalman Filter and LSTM neural network in hydrological modelling[J/OL]. Engineering Journal of Wuhan University, 2026, 1-12.
吕泳霖, 刘新波, 王鹏翔, 等. 基于集合卡尔曼滤波与长短期记忆神经网络的联合校正方法及其在径流模拟中的应用[J/OL]. 武汉大学学报（工学版）, 2026,1-12. DOI： 10.14188/j.1671-8844.2025.0095.

LV Yonglin, LIU Xinbo, WANG Pengxiang, et al. Application of combined correction method based on Ensemble Kalman Filter and LSTM neural network in hydrological modelling[J/OL]. Engineering Journal of Wuhan University, 2026, 1-12. DOI： 10.14188/j.1671-8844.2025.0095.

摘要

准确获取径流信息可为水资源评价、水资源调度和水资源管理与利用提供科学依据和数据支撑。本文基于三水源新安江模型，构建了一种集合卡尔曼滤波（EnKF）与长短期记忆神经网络（LSTM）的联合校正方法（EnKF-LSTM）。以岷沱江流域2010-2019年日尺度径流模拟为研究对象，综合评估了EnKF-LSTM、EnKF和LSTM的校正效果。研究结果表明，与校正前的初始三水源新安江模型结果对比来看，三种校正方法均可提升日径流过程模拟效果，其中EnKF-LSTM联合校正方法效果最为显著，LSTM次之，EnKF相对较差。相对于校正前，EnKF-LSTM可使岷江流域径流模拟NSE、R

和KGE分别提升25.37%、22.86%和13.92%；沱江流域径流模拟NSE、R

和KGE分别提升18.57%、22.85%和11.54%。从三种校正方法对丰水期与枯水期校正效果来看，EnKF-LSTM联合校正方法也均优于EnKF与LSTM。本文构建的EnKF-LSTM联合校正方法能够有效地提高径流模拟的精度，可为流域的径流模拟提供有效的技术支撑。

Abstract

Accurate runoff information is an important basis for the study of hydrological processes

which can provide scientific basis and data support for water resources management and utilization. In this study

we develop an integrated correction method (EnKF-LSTM) of ensemble Kalman filter (EnKF) and long short-term memory neural network (LSTM) based on the Xin'anjiang model. The Mintuo River Basin is selected as a case study area to comprehensively evaluate the correction effects of EnKF-LSTM

EnKF and LSTM. The results show that all the three calibration methods can improve the accuracy of daily runoff modelling

among which the EnKF-LSTM shows the best performance

followed by LSTM and EnKF. Compared with the pre-corrected scenario

the NSE

and KGE given by EnKF-LSTM increases by 25.37%

22.86% and 13.92% in the Minjiang River Basin

respectively

and 18.57%

22.85% and 11.54% in the Tuojiang River Basin

respectively. Moreover

the EnKF-LSTM outperforms both EnKF and LSTM in terms of both the flood and dry seasons. Therefore

the developed EnKF-LSTM in this study can effectively improve the accuracy of runoff simulations and provide effective technical support for runoff simulations in the basin.

关键词

Keywords

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