Study on performance of rainfall-runoff simulations using coupled long short-term memory network and Xin’anjiang model

季通焱; 黄鹏年; 李艳忠; 王洁

doi:10.11660/slfdxb.20240103

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Study on performance of rainfall-runoff simulations using coupled long short-term memory network and Xin’anjiang model

更新时间：2026-07-22

- Study on performance of rainfall-runoff simulations using coupled long short-term memory network and Xin’anjiang model
- Journal of Hydroelectric Engineering Vol. 43, Issue 1, (2024)
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- DOI：10.11660/slfdxb.20240103
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- Online First：25 January 2024，
  
  Published：2024
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季通焱, 黄鹏年, 李艳忠, et al. Study on performance of rainfall-runoff simulations using coupled long short-term memory network and Xin’anjiang model[J]. Journal of Hydroelectric Engineering, 2024, 43(1).
DOI：

季通焱, 黄鹏年, 李艳忠, et al. Study on performance of rainfall-runoff simulations using coupled long short-term memory network and Xin’anjiang model[J]. Journal of Hydroelectric Engineering, 2024, 43(1). DOI： 10.11660/slfdxb.20240103.

摘要

深度学习技术在降雨径流模拟方面具有广阔应用前景，但受训练样本限制，需与传统水文模型相耦合，由传统水文模型提供训练数据。耦合数据的选择和超参数方案对耦合模型的模拟性能影响显著，但尚未有专门的研究。本文以东湾流域为例，用双向长短时记忆网络耦合新安江模型不同模块数据，并用灰狼优化算法优化超参数，构建降雨径流模型。结果表明：模型耦合不同数据时，对日径流和场次洪水的模拟性能均有提高，尤以耦合产流量和模拟流量数据时最为明显。不同耦合数据需调整超参数方案，灰狼优化算法可满足需求。本研究为提高耦合模型径流模拟能力提供了新思路和新方法。

Abstract

Deep learning techniques have a promising application in rainfall-runoff simulations

but they are limited by the availability of training samples and need coupling with a traditional hydrological model that can provide training data. Selection of coupled data and hyperparameters has a significant impact on the simulation performance of a coupled model

but it lacks deep study. In this paper

we present a rainfall-runoff simulation model by coupling different module data of the Xin’anjiang model with a bidirectional long short-term memory network and optimizing the hyperparameters using the Grey Wolf optimization algorithm

along with an application to the Dongwan watershed. The results show the model improves the simulations of daily runoffs and flood events when coupled with different data

especially runoff data and simulated flow data. The hyperparameter scheme needs to be adjusted to different coupled data

and the Grey Wolf optimization algorithm can meet such a demand. This study provides new ideas and methods for enhancing the runoff simulation capability of the coupled models.

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