Runoff Intelligent Prediction Method Based on Broad-deep Fusion Time-frequency Analysis

doi:10.16182/j.issn1004731x.joss.22-1137

Abstract

Abstract:

Broad learning system(BLS) is introduced to tackle the existed disadvantage that LSTM-based runoff prediction model is easy to fall into local optimization. To reduce the influence of noise on the prediction results, the variational mode decomposition (VMD) is adopted to transform the one-dimensional time-domain runoff signal to the two-dimensional time-frequency plane. The runoff prediction model based on VMD-LSTM-BLS is proposed. The simulation results demonstrate that the prediction accuracy of the new model is more significantly improved compared with the baseline model and the existing LSTM-based runoff prediction model.

Key words: runoff forecast, variational mode decomposition, long and short-term memory network, broad learning system, time-frequency analysis, intelligent prediction

CLC Number:

TP391.9

Han Ying, Wang Lehao, Wang Shumei, Zhang Xiang, Luo Xingxing. Runoff Intelligent Prediction Method Based on Broad-deep Fusion Time-frequency Analysis[J]. Journal of System Simulation, 2024, 36(2): 363-372.

Figures/Tables 10

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Table 2

Table 3

Fig. 5

Fig. 6

Fig. 7

References 18

1	金菊良, 魏一鸣, 丁晶. 预测日径流过程的最近邻仿真模型[J]. 系统仿真学报, 2002, 14(11): 1494-1496.
	Jin Juliang, Wei Yiming, Ding Jing. Nearest Neighbor Bootstrap Model for Predicting Daily Flow Process[J]. Journal of System Simulation, 2002, 14(11): 1494-1496.
2	Huseyin Cagan Kilinc, Yurtsever Adem. Short-term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series[J]. Sustainability, 2022, 14(6): 3352.
3	李伟, 周玉臣, 林圣琳, 等. 仿真模型验证方法综述[J]. 系统仿真学报, 2019, 31(7): 1249-1256.
	Li Wei, Zhou Yuchen, Lin Shenglin, et al. Review of Simulation Model Validation Methods[J]. Journal of System Simulation, 2019, 31(7): 1249-1256.
4	Qin Jinbo, Liang Ji, Chen Tao, et al. Simulating and Predicting of Hydrological Time Series Based on TensorFlow Deep Learning[J]. Polish Journal of Environmental Studies, 2019, 28(2): 795-802.
5	Peng Tian, Zhou Jianzhong, Zhang Chu, et al. Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks[J]. Water, 2017, 9(6): 406.
6	曹爽, 秦天玲, 石晓晴, 等. 亚马逊流域降雨径流时空变化分析[J]. 水文, 2018, 38(1): 90-96.
	Cao Shuang, Qin Tianling, Shi Xiaoqing, et al. Time-spatial Distribution of Rainfall and Runoff in Amazon Basin[J]. Journal of China Hydrology, 2018, 38(1): 90-96.
7	黄克威, 王根绪, 宋春林, 等. 基于LSTM的青藏高原冻土区典型小流域径流模拟及预测[J]. 冰川冻土, 2021, 43(4): 1144-1156.
	Huang Kewei, Wang Genxu, Song Chunlin, et al. Runoff Simulation and Prediction of a Typical Small Watershed in Permafrost Region of the Qinghai-Tibet Plateau Based on LSTM[J]. Journal of Glaciology and Geocryology, 2021, 43(4): 1144-1156.
8	夏容, 江官星. 基于LSTM的软件时间序列延迟预测仿真[J]. 计算机仿真, 2021, 38(12): 435-439.
	Xia Rong, Jiang Guanxing. Software Time Series Delay Prediction Simulation Based on LSTM[J]. Computer Simulation, 2021, 38(12): 435-439.
9	Chen Xi, Huang Jiaxu, Han Zhen, et al. The Importance of Short Lag-time in the Runoff Forecasting Model Based on Long Short-term Memory[J]. Journal of Hydrology, 2020, 589: 125359.
10	王军, 高梓勋, 朱永明. 基于CNN-LSTM模型的黄河水质预测研究[J]. 人民黄河, 2021, 43(5): 96-99, 109.
	Wang Jun, Gao Zixun, Zhu Yongming. Research on Yellow River Water Quality Prediction Based on CNN-LSTM Model[J]. Yellow River, 2021, 43(5): 96-99, 109.
11	Tan Qiaofeng, Lei Xiaohui, Wang Xu, et al. An Adaptive Middle and Long-term Runoff Forecast Model Using EEMD-ANN Hybrid Approach[J]. Journal of Hydrology, 2018, 567: 767-780.
12	Dragomiretskiy K, Zosso D. Variational Mode Decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
13	包苑村, 解建仓, 罗军刚. 基于VMD-CNN-LSTM模型的渭河流域月径流预测[J]. 西安理工大学学报, 2021, 37(1): 1-8.
	Bao Yuancun, Xie Jiancang, Luo Jungang. Monthly Runoff Prediction of Weihe River Basin Based on VMD-CNN-LSTM Model[J]. Journal of Xi'an University of Technology, 2021, 37(1): 1-8.
14	任长娥, 袁超, 孙彦丽, 等. 宽度学习系统研究进展[J]. 计算机应用研究, 2021, 38(8): 2258-2267.
	Ren Chang'e, Yuan Chao, Sun Yanli, et al. Research of Broad Learning System[J]. Application Research of Computers, 2021, 38(8): 2258-2267.
15	Zhou Jianzhong, Peng Tian, Zhang Chu, et al. Data Pre-analysis and Ensemble of Various Artificial Neural Networks for Monthly Streamflow Forecasting[J]. Water, 2018, 10(5): 628.
16	李彤, 胡国华, 顾庆福, 等. 近55年来降水及人类活动对资水流域径流的影响[J]. 水文, 2018, 38(6): 54-58, 88.
	Li Tong, Hu Guohua, Gu Qingfu, et al. Impact of Precipitation and Human Activities on Runoff in Zishui River in Recent 55 Years[J]. Journal of China Hydrology, 2018, 38(6): 54-58, 88.
17	胡庆芳, 曹士圯, 杨辉斌, 等. 汉江流域安康站日径流预测的LSTM模型初步研究[J]. 地理科学进展, 2020, 39(4): 636-642.
18	Seo Youngmin, Kim Sungwon, Singh V P. Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-runoff[J]. Atmosphere, 2018, 9(7): 251.

K	IMF0	IMF1	IMF2	IMF3	IMF4	IMF5	IMF6
3	0.359	0.185	0.025
4	0.467	0.347	0.183	0.025
5	0.467	0.348	0.182	0.026	0.006
6	0.467	0.348	0.271	0.182	0.026	0.006
7	0.486	0.467	0.348	0.271	0.182	0.026	0.006

层	参数	值
Input	时间步长	2
ARIMA	{ p, d, q}	{10, 1, 0}
SVM	C	100 000
	缓冲大小	32
	γ	0.000 1
VMD	α	10
VMD	K	5
CNN	卷积核个数	512
	卷积核大小	5
	填充	same
	激活函数	ReLU
Maxpooling	池化核大小	2
Maxpooling	填充	valid
LSTM	层数	3
	神经元个数	{256, 128, 128}
	优化器	Adam
	批大小	20
	训练次数	80
	损失函数	MAE
Dropout	P	0.2
Dense	层数	2
	神经元个数	{10, 1}
	激活函数	ReLU
BLS	s	0.8
	C	0.000 1
	N₁	10
	N₂	30
	N₃	280

模型	E_RMS	E_MA	E_MAP	S_N
ARIMA	0.921	0.532	1.659	0.133
SVR	0.914	0.513	1.619	0.145
LSTM	0.858	0.494	1.422	0.247
CNN-LSTM	0.823	0.482	1.302	0.308
LSTM-BLS	0.795	0.453	1.025	0.353
VMD-LSTM	0.381	0.286	0.688	0.515
VMD-CNN-LSTM	0.282	0.266	0.174	0.877
本文	0.004	0.003	0.025	0.985

[1]	Tong Xiaorong. Blind Separation Simulation System of Sound Signals Based on Time-Frequency Analysis of Short Time Fourier Transformation [J]. Journal of System Simulation, 2019, 31(2): 353-359.
[2]	Xu Xinghua, Pan Tinglong, Wu Dinghui. Wind Speed Noise Reduction in Wind Farm Based on Variational Mode Decomposition [J]. Journal of System Simulation, 2019, 31(1): 87-93.
[3]	Jia Yafei, Zhu Yongli, Wang Liuwang. Time-frequency Analysis of Partial Discharge Signal Based on VMD and Wigner-Ville Distribution [J]. Journal of System Simulation, 2018, 30(2): 569-578.
[4]	Wang Tiantian, Wang Yan, Ji Zhicheng. Fault Diagnosis of Rolling Bearing Based on Improved Extreme Learning Machine [J]. Journal of System Simulation, 2018, 30(11): 4413-4420.
[5]	Chi Yulun, Li Haolin, Yue Tai. Study on Single-point Dresser Wear Intelligent Prediction Method [J]. Journal of System Simulation, 2017, 29(6): 1210-1217.