Zobrazeno 1 - 10
of 11
pro vyhledávání: '"CUI Dongwen"'
Autor:
WANG Yongshun, CUI Dongwen
Publikováno v:
Renmin Zhujiang, Vol 45, Pp 92-100 (2024)
The permanganate index (CODMn) is one of the important indicators for measuring the degree of pollution of water bodies by reducing substances. To improve the prediction accuracy of CODMn, a WPT-SHIO-NARX CODMn time series prediction model is propose
Externí odkaz:
https://doaj.org/article/ae88c08c6fb141b3ac867bc18c07dc81
Autor:
GAO Xuemei, CUI Dongwen
Publikováno v:
Renmin Zhujiang, Vol 45, p (2024)
Accurate multi-step sediment concentration prediction is of significance for regional soil erosion control,flood control and disaster reduction.To improve the multi-step prediction accuracy of sediment concentration and the prediction performance of
Externí odkaz:
https://doaj.org/article/6d39c5808aca47819e8f267e8177253e
Autor:
WANG Yongshun, CUI Dongwen
Publikováno v:
Renmin Zhujiang, Vol 44 (2023)
Total nitrogen (TN) is one of the important indicators to reflect the degree of water pollution and measure the eutrophication status of lakes and reservoirs.To improve the accuracy of TN prediction,based on the empirical wavelet transform (EWT) and
Externí odkaz:
https://doaj.org/article/837a5a462364405ba79e79b3fc7f0315
Autor:
LI Lude, CUI Dongwen
Publikováno v:
Renmin Zhujiang, Vol 43 (2022)
Considering the nonlinear and multi-scale characteristics of hydrological time series,this paper proposes a squirrel search algorithm (SSA)-extreme learning machine (ELM) forecasting model based on wavelet packet decomposition (WPD) and phase space r
Externí odkaz:
https://doaj.org/article/f280aecf5e694d9a930da3ed493ccac3
Autor:
ZHANG Yajie, CUI Dongwen
Publikováno v:
Renmin Zhujiang, Vol 43 (2022)
To improve the accuracy of monthly runoff forecasts during dry seasons,this study proposes a forecasting method that combines the golden eagle optimization (GEO) algorithm and the relevance vector machine (RVM).On the basis of the runoff data of 67 a
Externí odkaz:
https://doaj.org/article/23dfa8b2c8184281a14e05680f627752
Autor:
ZHANG Yajie, CUI Dongwen
Publikováno v:
Renmin Zhujiang, Vol 43 (2022)
In view of the multi-scale non-stationarity and other characteristics of monthly runoff in hydrological time series,this paper proposes a singular spectrum decomposition (SSD)-based model of combined monthly runoff forecasting that integrates the stu
Externí odkaz:
https://doaj.org/article/3921455221d64b1e9b4edcf9b9104981
Autor:
LI Xinhua, CUI Dongwen
Publikováno v:
Renmin Zhujiang, Vol 43 (2022)
This paper studies a prediction method combining the artificial electric field algorithm (AEFA) and extreme learning machine (ELM) to improve the accuracy of dam deformation prediction.With the 72nd dam settlement data of Guandi Hydropower Station as
Externí odkaz:
https://doaj.org/article/7d6e3080259b4a49bb9b0184fc4f2a0c
Autor:
ZHANG Yajie, CUI Dongwen
Publikováno v:
Renmin Zhujiang, Vol 43 (2022)
To improve the accuracy of runoff prediction,this paper proposes a runoff prediction method based on empirical mode decomposition (EMD),forensic-based investigation (FBI) algorithm and extreme learning machine (ELM).Firstly,EMD is used to decompose t
Externí odkaz:
https://doaj.org/article/3e671c73a0b849a59f67a0c62d208765
Autor:
LIANG Xiaoxin, CUI Dongwen
Publikováno v:
Renmin Zhujiang, Vol 43 (2022)
According to the idea of sequence decomposition-parameter optimization-subitem prediction-result superposition,we construct a medium and long-term monthly runoff prediction model integrating singular spectrum analysis (SSA)-gradient-based optimizatio
Externí odkaz:
https://doaj.org/article/2db531d48dba430982c156091981012f
Autor:
HU Shunqiang, CUI Dongwen
Publikováno v:
Renmin Zhujiang, Vol 42 (2021)
To improve the accuracy of runoff prediction,this paper proposes a runoff prediction model based on the combination of empirical mode decomposition (EMD),long short-term memory (LSTM) neural network,and adaptive neuro-fuzzy inference system (ANFIS),d
Externí odkaz:
https://doaj.org/article/f3c43ce7b2d5444ab2355c4247b05e32