Combined model for medium- and long-term runoff predictions based on Extreme-point Symmetric Mode Decomposition

李继清; 刘洋; 张鹏; 陈景

doi:10.11660/slfdxb.20240703

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Combined model for medium- and long-term runoff predictions based on Extreme-point Symmetric Mode Decomposition

更新时间：2026-06-30

- Combined model for medium- and long-term runoff predictions based on Extreme-point Symmetric Mode Decomposition
- Journal of Hydroelectric Engineering Vol. 43, Issue 7, (2024)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11660/slfdxb.20240703
  CLC：
- Online First：25 July 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
李继清, 刘洋, 张鹏, et al. Combined model for medium- and long-term runoff predictions based on Extreme-point Symmetric Mode Decomposition[J]. Journal of Hydroelectric Engineering, 2024, 43(7).
DOI：

李继清, 刘洋, 张鹏, et al. Combined model for medium- and long-term runoff predictions based on Extreme-point Symmetric Mode Decomposition[J]. Journal of Hydroelectric Engineering, 2024, 43(7). DOI： 10.11660/slfdxb.20240703.

摘要

为提高径流预报精度，解决径流序列分解后高频分量波动范围大、预报精度差的问题，基于极点对称模态分解法（ESMD）平稳化处理技术将径流序列分解，通过分析不同频率分量特征，择优选取预报方法，结合粒子群优化最小二乘支持向量机（PSO-LSSVM）全局优化和非线性建模能力及适应性强的特点，对高频分量进行预测，利用BP神经网络非线性映射能力和逼近任意非线性函数的优势对中低频分量和趋势分量进行预报，构建了ESMD-PSO-LSSVM-BP组合预报模型，对西江干流上中下游三座水文站的年、月尺度径流开展中长期径流预报。结果表明，对不同频率分量采用不同预报方法的组合模型可以有效提高径流预报精度。

Abstract

Extreme-point Symmetric Mode Decomposition (ESMD) is used to predict runoff series based on a runoff forecasting model to solve two problems after runoff series decomposition－large fluctuation ranges of high frequency components and poor forecast accuracy. We use the stationary processing technique of the ESMD to decompose the runoff series

select the best prediction method by analyzing the characteristics of different frequency components

combine Particle Swarm Optimization (PSO) and Least Square Support Vector Machines (LSSVM) for the prediction of high-frequency components

and use the back-propogation (BP) neural network for the prediction of mid- and low-frequency components. A combined ESMD-PSO-LSSVM-BP forecasting model is constructed to forecast annual and monthly runoffs at three hydrological stations in the upper and middle reaches of the Xijiang River. The results show this model

using different forecasting methods for different frequency components

improves the runoff forecasting accuracy significantly.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Runoff forecasts using combined model of extreme-point symmetric mode decomposition and Elman neural network

Study on graph neural network-based runoff forecasting model for medium and small-sized watersheds. A case study of Shaxi watershed in Fujian

Diagnosis and driving force analysis of variations in precipitation- temperature relation of Xijiang River basin

Characteristics of precipitation in Xijiang River basin from 1970 to 2017

Spatiotemporal evolution of runoff based on extreme-point symmetric mode decomposition

Related Author

TIAN Yu

WU Yueqiu

LI Jiqing

WANG Shuang

魏加华

张文倩

高帅

罗火钱

Related Institution

School of Water Resources and Hydropower Engineering, North China Electric Power University

China Institute of Water Resources and Hydropower Research

College of Water Sciences, Beijing Normal University

Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology

School of Engineering,Anhui Agricultural University

AI问答

⁰