Hydrological time series forecasting based on WD-RSPA model——A case study of Makou Station and Shenzhen Station

LIU Zufa; TAN Mingxin; ZHA Xini; ZHUO Wenshan; ZHOU Yueying; CHEN Jichen; YAO Hanmei

您当前的位置：

首页 >

文章列表页 >

Hydrological time series forecasting based on WD-RSPA model——A case study of Makou Station and Shenzhen Station

更新时间：2023-12-11

- Hydrological time series forecasting based on WD-RSPA model——A case study of Makou Station and Shenzhen Station
- Acta Scientiarum Naturalium Universitatis SunYatseni Vol. 56, Issue 5, Pages: 119-126(2017)
- 作者机构：
  
  1. 中山大学水资源与环境研究中心∥华南地区水循环与水安全广东省普通高校重点实验室,广东,广州,510275
  2.  2中山大学新华学院,广东,广州,510520
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017，
  
  Published Online：25 September 2017，
扫描看全文
LIU Zufa, TAN Mingxin, ZHA Xini, et al. Hydrological time series forecasting based on WD-RSPA model——A case study of Makou Station and Shenzhen Station. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 56(5):119-126(2017)
DOI：

LIU Zufa, TAN Mingxin, ZHA Xini, et al. Hydrological time series forecasting based on WD-RSPA model——A case study of Makou Station and Shenzhen Station. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 56(5):119-126(2017) DOI：

摘要

为了解决传统水文时间序列预测模型预测精度不高、未考虑实际噪声影响等问题，将小波消噪（Wavelet De-noise

WD）与秩次集对分析（Rank Set Pair Analysis

RSPA）方法耦合，建立基于小波消噪的秩次集对分析水文预测模型（WD-RSPA），并应用于马口站年总径流量以及深圳市年总降雨量预测。结果表明：当集合维数T=4时，coif3RSPA模型预测马口站径流量的预测误差|e|=11.97%；T=6时，db5RSPA模型预测深圳市降雨量的预测误差|e|=17.73%。相较于传统AR(1)模型和单一的RSPA模型，WD-RSPA模型更接近真实值，是一种切实可行的水文时间序列预测方法。

Abstract

The traditional hydrological time series prediction models cannot get good prediction accuracy and they generally do not consider the actual noise. A new kind of hydrological forecasting model (Wavelet De-noise and Rank Set Pair Analysis forecasting model

WD-RSPA forecasting model) was built based on wavelet de-noising and set pair analysis

and was applied to predict the annual runoff of Makou Station and the annual precipitation of Shenzhen Station. The results show that when the dimension T=4

the prediction error of Makou Station with coif3RSPA model is 11.97%; and when T=6

the prediction error of Shenzhen Station with db5-RSPA model is 17.73%. Compared to the results predicted by AR(1) model and RSPA model

the results of WD-RSPA model are closer to the observed data

indicating that it is a practicable method to predict the hydrological time series.

关键词

水文时间序列小波消噪秩次集对分析年径流量预测年降雨量预测

Keywords

hydrological time serieswavelet de-noiserank set pair analysisannual runoff forecastingannual precipitation forecasting

references

Views

142

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

Postal code：510275
Tel：020-84112585，84113223 Email：xuebaozr@mail.sysu.edu.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰