Hydrological meaning and application of phase space reconstruction of runoff series

被引：0

作者：

Li J. ^{[1
,2
]}

He Q. ^{[1
]}

Wang S. ^{[3
]}

Wang X. ^{[1
,2
]}

Zhang J. ^{[1
]}

机构：

[1] School of Resources & Environment, Henan Polytechnic University, Jiaozuo

[2] State Collaborative Innovation Center of Coal Work Safety and Clean-Efficiency Utilization, Jiaozuo

[3] Zhejiang Geological Prospecting Institute of China Chemical Geology and Mine Bureau, Hangzhou

来源：

Water Resources Protection | 2024年 / 40卷 / 03期

关键词：

Chaos-BPNN runoff prediction model; chaotic characteristics; influencing factors of runoff; phase space reconstruction; runoff series;

D O I：

10.3880/j.issn.10046933.2024.03.011

中图分类号：

学科分类号：

摘要：

To determine the hydrological meaning of the reconstructed phase space of the runoff series and improve the accuracy of mid- to long-term runoff prediction, the phase space of the runoff series was reconstructed based on chaos theory, and correlation analysis was conducted between the influencing factors of runoff and the reconstructed phase space column vectors. On this basis, a runoff prediction model coupled with Chaos theory and artificial neural network (Chaos-BPNN) was established, and applied to the Yingluoxia and Zhengyixia hydrological stations in the upper reaches of the Heihe River. The results indicate that the reconstructed phase space column vectors of the runoff series have clear hydrological meaning. The Chaos-BPNN runoff prediction model only requires runoff sequence data for modeling and prediction, avoiding the problems of difficult determination and quantification of main control factors in the runoff prediction process. The precipitation, sediment transport, water level, and temperature in the upper reaches of the Heihe River are highly correlated with the reconstructed phase space columns 1, 3, 6, and 7, respectively. Wind speed is not correlated with any column, and it is speculated that factors such as snow line elevation, vegetation coverage, and land use type are correlated with columns 2, 4, and 5. The Chaos-BPNN runoff prediction model constructed has a runoff prediction accuracy of over 86% at the Yingluoxia and Zhengyixia hydrological stations in the upper reaches of the Heihe River. © 2024 Editorial Board of Water Resources Protection. All rights reserved.

引用

页码：90 / 97and148

共 24 条

[1] ZHANG Jinping, WANG Yuhao, Uncertainty analysis of rainfall-runoff relationship in Zhengzhou City [ J ], Water Resources Protection, 37, 6, pp. 1-6, (2021)
[2] LI Jianlin, WANG Shuwei, WANG Xinyi, Et al., Analysis on runoff characteristics of recurrence plot theory under different time scales, Journal of Basic Science and Engineering, 31, 3, pp. 569-583, (2023)
[3] YUE Zhaoxin, AI Ping, XIONG Chuansheng, Et al., Medium-long term runoff forecasting based on information entropy and improved extreme learning machine [ J], Advances in Science and Technology of Water Resources, 41, 4, pp. 7-14, (2021)
[4] LI Z L, LI Q J, WANG J, Et al., Impacts of projected climate change on runoff in upper reach of Heihe River basin using climate elasticity method and GCMs [ J ], Science of the Total Environment, 716, (2020)
[5] LI Zhenyang, LI Guofang, GAO Qianyu, Et al., Runoff response of Fulaerji station in Nenjiang River under changing environment [ J ], Advances in Science and Technology of Water Resources, 42, 2, pp. 35-40, (2022)
[6] SUN Zhouliang, LIU Yanli, ZHANG Jianyun, Et al., Research progress and prospect of mid-long term runoff prediction, Water Resources Protection, 39, 2, pp. 136-144, (2023)
[7] MA Panpan, BAI Tao, WU Lianzhou, Et al., Short-term runoff prediction by ANFIS model in source region of the Yellow River [ J], Journal of Northwest A & F University (Natural Science Edition), 46, 6, pp. 145-154, (2018)
[8] ZHOU Jianzhong, PENG Tian, Chaotic dynamic characteristics and integrated prediction of runoff in the upper reaches of Yangtze River, Journal of Changjiang River Scientific Research Institute, 35, 10, pp. 1-9, (2018)
[9] TIAN Ye, TAN Weili, WANG Guoqing, Et al., Performance of variant LSTM models in runoff prediction and their interpretability, Water Resources Protection, 39, 3, pp. 188-194, (2023)
[10] CHEN Zihao, LI Yingying, LI Kai, Et al., Trend prediction of incoming water process in the upper reaches of Heihe River based on M-K, wavelet and R/ S method [J], Yellow River, 43, 12, pp. 29-34, (2021)

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