干旱区生态环境知识资源中心(Arid): LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China

Arid

DOI	10.3390/w14111745
	LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China
	Li, Jiaxin; Qian, Kaixuan; Liu, Yuan; Yan, Wei; Yang, Xiuyun; Luo, Geping; Ma, Xiaofei
通讯作者	Ma, XF
来源期刊	WATER
EISSN	2073-4441
出版年	2022
卷号	14 期号:11
英文摘要	Inland river runoff variations in arid regions play a decisive role in maintaining regional ecological stability. Observation data of inland river runoff in arid regions have short time series and imperfect attributes due to limitations in the terrain environment and other factors. These shortages not only restrict the accurate simulation of inland river runoff in arid regions significantly, but also influence scientific evaluation and management of the water resources of a basin in arid regions. In recent years, research and applications of machine learning and in-depth learning technologies in the hydrological field have been developing gradually around the world. However, the simulation accuracy is low, and it often has over-fitting phenomenon in previous studies due to influences of complicated characteristics such as unsteady runoff. Fortunately, the circulation layer of Long-Short Term Memory (LSTM) can explore time series information of runoffs deeply to avoid long-term dependence problems. In this study, the LSTM algorithm was introduced and improved based on the in-depth learning theory of artificial intelligence and relevant meteorological factors that were monitored by coupling runoffs. The runoff data of the Yarkant River was chosen for training and test of the LSTM model. The results demonstrated that Mean Absolute Error (MAE) and Root Mean Square error (RMSE) of the LSTM model were 3.633 and 7.337, respectively. This indicates that the prediction effect and accuracy of the LSTM model were significantly better than those of the convolution neural network (CNN), Decision Tree Regressor (DTR) and Random Forest (RF). Comparison of accuracy of different models made the research reliable. Hence, time series data was converted into a problem of supervised learning through LSTM in the present study. The improved LSTM model solved prediction difficulties in runoff data to some extent and it applied to hydrological simulation in arid regions under several climate scenarios. It not only decreased runoff prediction uncertainty brought by heterogeneity of climate models and increased inland river runoff prediction accuracy in arid regions, but also provided references to basin water resource management in arid regions. In particular, the LSTM model provides an effective solution to runoff simulation in regions with limited data.
英文关键词	deep learning Long-Short Term Memory (LSTM) inland river in arid region runoff prediction water resource management
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:000808927200001
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; CLIMATE-CHANGE IMPACTS ; WATER-RESOURCES ; TIME-SERIES ; BASIN ; MANAGEMENT ; PRECIPITATION ; VARIABILITY ; GENERATION ; CHALLENGES
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/2XILL650/394792
推荐引用方式 GB/T 7714	Li, Jiaxin,Qian, Kaixuan,Liu, Yuan,et al. LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China[J],2022,14(11).
APA	Li, Jiaxin.,Qian, Kaixuan.,Liu, Yuan.,Yan, Wei.,Yang, Xiuyun.,...&Ma, Xiaofei.(2022).LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China.WATER,14(11).
MLA	Li, Jiaxin,et al."LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China".WATER 14.11(2022).