干旱区生态环境知识资源中心(Arid): Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Arid

DOI	10.3390/w15183222
	Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models
	Wang, Shuyang; Sun, Meiping; Wang, Guoyu; Yao, Xiaojun; Wang, Meng; Li, Jiawei; Duan, Hongyu; Xie, Zhenyu; Fan, Ruiyi; Yang, Yang
通讯作者	Sun, MP
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:18
英文摘要	Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteorological and hydrological stations in the HCMA and the inconsistency of the observed time series, the simulation and reconstruction of mountain runoff have always been a focus of cold region hydrological research. Based on the runoff observations of the Yurungkash and Kalakash Rivers, the upstream tributaries of the Hotan River on the northern slope of the Kunlun Mountains at different time periods, and the meteorological and atmospheric circulation indices, we used feature analysis and machine learning methods to select the input elements, train, simulate, and select the preferences of the machine learning models of the runoffs of the two watersheds, and reconstruct the missing time series runoff of the Kalakash River. The results show the following. (1) Air temperature is the most important driver of runoff variability in mountainous areas upstream of the Hotan River, and had the strongest performance in terms of the Pearson correlation coefficient (& rho;XY) and random forest feature importance (FI) (& rho;XY = 0.63, FI = 0.723), followed by soil temperature (& rho;XY = 0.63, FI = 0.043), precipitation, hours of sunshine, wind speed, relative humidity, and atmospheric circulation were weakly correlated. A total of 12 elements were selected as the machine learning input data. (2) Comparing the results of the Yurungkash River runoff simulated by eight machine learning methods, we found that the gradient boosting and random forest methods performed best, followed by the AdaBoost and Bagging methods, with Nash-Sutcliffe efficiency coefficients (NSE) of 0.84, 0.82, 0.78, and 0.78, while the support vector regression (NSE = 0.68), ridge (NSE = 0.53), K-nearest neighbor (NSE = 0.56), and linear regression (NSE = 0.51) were simulated poorly. (3) The application of four machine learning methods, gradient boosting, random forest, AdaBoost, and bagging, to simulate the runoff of the Kalakash River for 1978-1998 was generally outstanding, with the NSE exceeding 0.75, and the results of reconstructing the runoff data for the missing period (1999-2019) could well reflect the characteristics of the intra-annual and inter-annual changes in runoff.
英文关键词	feature analysis Hotan River Basin high-cold mountains area machine learning runoff simulation and reconstruction
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:001072200300001
WOS关键词	RIVER-BASIN ; WATER-RESOURCES ; CLIMATE-CHANGE ; IMPACT ; STREAMFLOW
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/2XILL650/399074
推荐引用方式 GB/T 7714	Wang, Shuyang,Sun, Meiping,Wang, Guoyu,et al. Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models[J],2023,15(18).
APA	Wang, Shuyang.,Sun, Meiping.,Wang, Guoyu.,Yao, Xiaojun.,Wang, Meng.,...&Yang, Yang.(2023).Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models.WATER,15(18).
MLA	Wang, Shuyang,et al."Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models".WATER 15.18(2023).