气候变化领域集成服务门户(CCPortal): Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory

CCPortal

DOI	10.1007/s11269-022-03414-8
	Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory
	Wu, Junhao; Wang, Zhaocai; Hu, Yuan; Tao, Sen; Dong, Jinghan
发表日期	2023
ISSN	0920-4741
EISSN	1573-1650
起始页码	937
结束页码	953
卷号	37 期号:2
英文摘要	Water resources matters considerably in maintaining the biological survival and sustainable socio-economic development of a region. Affected by a combination of factors such as geographic characteristics of the basin and climate change, runoff variability is non-linear and non-stationary. Runoff forecasting is one of the important engineering measures to prevent flood disasters. The improvement of its accuracy is also a difficult problem in the research of water resources management. To this end, an ensemble deep learning model was hereby developed to forecast daily river runoff. First, variational mode decomposition (VMD) was used to decompose the original daily runoff series data set into discrete internal model function (IMF) and distinguish signals with different frequencies. Then, for each IMF, a convolutional neural network (CNN) was introduced to extract the features of each IMF component. Subsequently, a bi-directional long short-term memory network (BiLSTM) based on an attention mechanism (AM) was used for prediction. A Bayesian optimization algorithm (BOA) was also introduced to optimize the hyperparameters of the BiLSTM, thereby further improving the estimation precision of the VMD-CNN-AM-BOA-BiLSTM model. The model was applied to the daily runoff data from January 1, 2010 to November 30, 2021 at the Wushan and Weijiabao Hydrological Stations in the Wei River Basin, and the RMSEs of 3.54 and 15.23 were obtained for the test set data at the two stations respectively, which were much better than those of EEMD-VMD-SVM and CNN-BiLSTM-AM models. Additionally, the hereby proposed model is proven to have better peak flood prediction capability and adaptability under different hydrological environments. Based on this sound performance, the model becomes an effective data-driven tool in hydrological forecasting practice, and can also provide some reference and practical application guidance for water resources management and flood warning.
英文关键词	Attention mechanism; Bayes optimization algorithm; Bi-directional long short-term memory; Convolutional neural networks; Daily runoff prediction; Variational mode decomposition
语种	英语
WOS研究方向	Engineering, Civil ; Water Resources
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000911236700001
来源期刊	WATER RESOURCES MANAGEMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281561
作者单位	Shanghai Ocean University; Shanghai Ocean University; East China University of Science & Technology; Shanghai Ocean University
推荐引用方式 GB/T 7714	Wu, Junhao,Wang, Zhaocai,Hu, Yuan,et al. Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory[J],2023,37(2).
APA	Wu, Junhao,Wang, Zhaocai,Hu, Yuan,Tao, Sen,&Dong, Jinghan.(2023).Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory.WATER RESOURCES MANAGEMENT,37(2).
MLA	Wu, Junhao,et al."Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory".WATER RESOURCES MANAGEMENT 37.2(2023).