干旱区生态环境知识资源中心(Arid): Evaluation of Soil and Water Assessment Tool and Artificial Neural Network models for hydrologic simulation in different climatic regions of Asia

Arid

DOI	10.1016/j.scitotenv.2019.134308
	Evaluation of Soil and Water Assessment Tool and Artificial Neural Network models for hydrologic simulation in different climatic regions of Asia
	Pradhan, Pragya; Tingsanchali, Tawatchai; Shrestha, Sangam
通讯作者	Shrestha, Sangam
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2020
卷号	701
英文摘要	In this study, a physically-based hydrological model, Soil and Water Assessment Tool (SWAT) and three types of Artificial Neural Network (ANN) models were used to simulate daily streamflow, and results were compared with observed data for performance analysis. The study was carried out in three different river basins with three different climatic characteristics, namely the West-Seti River Basin in a subtropical (partially wet) climatic region, Sre Pok River Basin in a tropical (wet) climatic region and Hari Rod River Basin in a semi-arid (dry) climatic region. The SWAT and ANN models were evaluated using statistical indicators such as the correlation coefficient (R-2), Nash-Sutcliffe efficiency (NSE), and percentage bias (PBIAS). The performance of ANN models was found to be very good with both R-2 and NSE values greater than 0.95 for the training and validation periods in the West-Seti River Basin and Sre Pok River Basin. Whereas, in the Hari Rod River Basin, the performance of the SWAT model was good with both R-2 and NSE values greater than 0.60 for the calibration and validation periods. Moreover, the performance of SWAT and ANN models was evaluated based on hydrological indicators (i.e. annual discharge, base flow, Q(dry), and Q(wet)), during different flow periods (very high to very low flow) using flow duration curves (FDCs). The SWAT model was found to be better for low flow simulation and the ANN model performed better for high flow simulation in the three river basins. (C) 2019 Elsevier B.V. All rights reserved.
英文关键词	SWAT ANN Hydrological indicators Semi-arid Tropical Sub-tropical
类型	Article
语种	英语
国家	Thailand
收录类别	SCI-E
WOS记录号	WOS:000498801400028
WOS关键词	SWAT ; RUNOFF ; BASIN
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
EI主题词	2020-01-20
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/312546
作者单位	Asian Inst Technol, Sch Engn & Technol, Water Engn & Management, Pathum Thani 12120, Thailand
推荐引用方式 GB/T 7714	Pradhan, Pragya,Tingsanchali, Tawatchai,Shrestha, Sangam. Evaluation of Soil and Water Assessment Tool and Artificial Neural Network models for hydrologic simulation in different climatic regions of Asia[J],2020,701.
APA	Pradhan, Pragya,Tingsanchali, Tawatchai,&Shrestha, Sangam.(2020).Evaluation of Soil and Water Assessment Tool and Artificial Neural Network models for hydrologic simulation in different climatic regions of Asia.SCIENCE OF THE TOTAL ENVIRONMENT,701.
MLA	Pradhan, Pragya,et al."Evaluation of Soil and Water Assessment Tool and Artificial Neural Network models for hydrologic simulation in different climatic regions of Asia".SCIENCE OF THE TOTAL ENVIRONMENT 701(2020).