干旱区生态环境知识资源中心(Arid): Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting

Arid

DOI	10.3390/w12051500
	Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting
	Apaydin, Halit; Feizi, Hajar; Sattari, Mohammad Taghi; Colak, Muslume Sevba; Shamshirband, Shahaboddin; Chau, Kwok-Wing
通讯作者	Sattari, MT
来源期刊	WATER
EISSN	2073-4441
出版年	2020
卷号	12 期号:5
英文摘要	Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012-2018, and all models are coded in Python software programming language. Only delays of streamflow time series are used as the input of models. Then, based on the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency coefficient (NS), results of deep-learning architectures are compared with one another and with an artificial neural network (ANN) with two hidden layers. Results indicate that the accuracy of deep-learning RNN methods are better and more accurate than ANN. Among methods used in deep learning, the LSTM method has the best accuracy, namely, the simulated streamflow to the dam reservoir with 90% accuracy in the training stage and 87% accuracy in the testing stage. However, the accuracies of ANN in training and testing stages are 86% and 85%, respectively. Considering that the Ermenek Dam is used for hydroelectric purposes and energy production, modeling inflow in the most realistic way may lead to an increase in energy production and income by optimizing water management. Hence, multi-percentage improvements can be extremely useful. According to results, deep-learning methods of RNNs can be used for estimating streamflow to the Ermenek Dam reservoir due to their accuracy.
英文关键词	streamflow time series simulation deep learning Ermenek Bi-LSTM
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000555915200279
WOS关键词	SOHU STREAM ; LSTM
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/325427
作者单位	[Apaydin, Halit; Sattari, Mohammad Taghi; Colak, Muslume Sevba] Ankara Univ, Fac Agr, Dept Agr Engn, TR-06110 Ankara, Turkey; [Feizi, Hajar; Sattari, Mohammad Taghi] Univ Tabriz, Agr Fac, Dept Water Engn, Tabriz 51666, Iran; [Shamshirband, Shahaboddin] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam; [Shamshirband, Shahaboddin] Ton Duc Thang Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam; [Chau, Kwok-Wing] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Apaydin, Halit,Feizi, Hajar,Sattari, Mohammad Taghi,et al. Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting[J],2020,12(5).
APA	Apaydin, Halit,Feizi, Hajar,Sattari, Mohammad Taghi,Colak, Muslume Sevba,Shamshirband, Shahaboddin,&Chau, Kwok-Wing.(2020).Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting.WATER,12(5).
MLA	Apaydin, Halit,et al."Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting".WATER 12.5(2020).