干旱区生态环境知识资源中心(Arid): Deep Learning Approach with LSTM for Daily Streamflow Prediction in a Semi-Arid Area: A Case Study of Oum Er-Rbia River Basin, Morocco

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DOI	10.3390/w15020262
	Deep Learning Approach with LSTM for Daily Streamflow Prediction in a Semi-Arid Area: A Case Study of Oum Er-Rbia River Basin, Morocco
	Nifa, Karima; Boudhar, Abdelghani; Ouatiki, Hamza; Elyoussfi, Haytam; Bargam, Bouchra; Chehbouni, Abdelghani
通讯作者	Nifa, K
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:2
英文摘要	Daily hydrological modelling is among the most challenging tasks in water resource management, particularly in terms of streamflow prediction in semi-arid areas. Various methods were applied in order to deal with this complex phenomenon, but recently data-driven models have taken a better space, given their ability to solve prediction problems in time series. In this study, we have employed the Long Short-Term Memory (LSTM) network to simulate the daily streamflow over the Ait Ouchene watershed (AIO) in the Oum Er-Rbia river basin in Morocco, based on a temporal sequence of in situ and remotely sensed hydroclimatic data ranging from 2001 to 2010. The analysis adopted in this work is based on three-dimension input required by the LSTM model (1); the input samples used three splitting approaches: 70% of the dataset as training, splitting the data considering the hydrological year and the cross-validation method; (2) the sequence length; (3) and the input features using two different scenarios. The prediction results demonstrate that the LSTM performs poorly using the default data input scenario, whereas the best results during the testing were found in a sequence length of 30 days using approach 3 (R-2 = 0.58). In addition, the LSTM fed with the lagged data input scenario using the Forward Feature Selection (FFS) method provides high performance accuracy using approach 2 (R-2 = 0.84) in a sequence length of 20 days. Eventually, in applications related to water resources management where data are limited, the use of the deep learning technique is able to create high predictive accuracy, which can be enhanced with the right combination subset of features by using FFS.
英文关键词	water resource management semi-arid region daily streamflow prediction deep learning LSTM sequence length FFS
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:000918937700001
WOS关键词	MODEL ; SIMULATIONS ; WATERSHEDS ; FORECASTS ; PRODUCTS ; DROUGHT ; NETWORK
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/2XILL650/398979
推荐引用方式 GB/T 7714	Nifa, Karima,Boudhar, Abdelghani,Ouatiki, Hamza,et al. Deep Learning Approach with LSTM for Daily Streamflow Prediction in a Semi-Arid Area: A Case Study of Oum Er-Rbia River Basin, Morocco[J],2023,15(2).
APA	Nifa, Karima,Boudhar, Abdelghani,Ouatiki, Hamza,Elyoussfi, Haytam,Bargam, Bouchra,&Chehbouni, Abdelghani.(2023).Deep Learning Approach with LSTM for Daily Streamflow Prediction in a Semi-Arid Area: A Case Study of Oum Er-Rbia River Basin, Morocco.WATER,15(2).
MLA	Nifa, Karima,et al."Deep Learning Approach with LSTM for Daily Streamflow Prediction in a Semi-Arid Area: A Case Study of Oum Er-Rbia River Basin, Morocco".WATER 15.2(2023).