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Article

LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China

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Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830046, China
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State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
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Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, College of Geographic Science and Tourism, Xinjiang Normal University, Urumqi 830054, China
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School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China
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Research Centre for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China
*
Author to whom correspondence should be addressed.
Academic Editors: Hristos Tyralis and Georgia Papacharalampous
Water 2022, 14(11), 1745; https://doi.org/10.3390/w14111745
Received: 22 April 2022 / Revised: 25 May 2022 / Accepted: 27 May 2022 / Published: 29 May 2022
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. View Full-Text
Keywords: deep learning; Long-Short Term Memory (LSTM); inland river in arid region; runoff prediction; water resource management deep learning; Long-Short Term Memory (LSTM); inland river in arid region; runoff prediction; water resource management
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MDPI and ACS Style

Li, J.; Qian, K.; Liu, Y.; Yan, W.; Yang, X.; Luo, G.; Ma, X. LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China. Water 2022, 14, 1745. https://doi.org/10.3390/w14111745

AMA Style

Li J, Qian K, Liu Y, Yan W, Yang X, Luo G, Ma X. LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China. Water. 2022; 14(11):1745. https://doi.org/10.3390/w14111745

Chicago/Turabian Style

Li, Jiaxin, Kaixuan Qian, Yuan Liu, Wei Yan, Xiuyun Yang, Geping Luo, and Xiaofei Ma. 2022. "LSTM-Based Model for Predicting Inland River Runoff in Arid Region: A Case Study on Yarkant River, Northwest China" Water 14, no. 11: 1745. https://doi.org/10.3390/w14111745

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