干旱区生态环境知识资源中心(Arid): Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)

Arid

DOI	10.3390/su14126974
	Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)
	Jayanthi, Sri Lakshmi Sesha Vani; Keesara, Venkata Reddy; Sridhar, Venkataramana
通讯作者	Sridhar, V
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2022
卷号	14 期号:12
英文摘要	Lakes are major surface water resource in semi-arid regions, providing water for agriculture and domestic use. Prediction of future water availability in lakes of semi-arid regions is important as they are highly sensitive to climate variability. This study is to examine the water level fluctuations in Pakhal Lake, Telangana, India using a combination of a process-based hydrological model and machine learning technique under climate change scenarios. Pakhal is an artificial lake built to meet the irrigation requirements of the region. Predictions of lake level can help with effective planning and management of water resources. In this study, an integrated approach is adopted to predict future water level fluctuations in Pakhal Lake in response to potential climate change. This study makes use of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset which contains 21 Global Climate Models (GCMs) at a resolution of 0.25 x 0.25 degrees is used for the study. The Reliability Ensemble Averaging (REA) method is applied to the 21 models to create an ensemble model. The hydrological model outputs from Soil and Water Assessment Tool (SWAT) are used to develop the machine-learning based Support Vector Regression (nu-SVR) model for predicting future water levels in Pakhal Lake. The scores of the three metrics, correlation coefficient (R-2), RMSE and MEA are 0.79, 0.018 m, and 0.13 m, respectively for the training period. The values for the validation periods are 0.72, 0.6, and 0.25 m, indicating that the model captures the observed lake water level trends satisfactorily. The SWAT simulation results showed a decrease in surface runoff in the Representative Concentration Pathways (RCP) 4.5 scenario and an increase in the RCP 8.5 scenario. Further, the results from nu-SVR model for the future time period indicate a decrease in future lake levels during crop growth seasons. This study aids in planning of necessary water management options for Pakhal Lake under climate change scenarios. With limited observed datasets, this study can be easily extended to the other lake systems.
英文关键词	climate change lakes NEX-GDDP support vector regression SWAT model
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000816792000001
WOS关键词	CLIMATE-CHANGE IMPACT ; RIVER-BASIN ; ARTIFICIAL-INTELLIGENCE ; BIAS CORRECTION ; DATA SET ; MODEL ; SYSTEM ; UNCERTAINTY ; LEVEL ; SIMULATIONS
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394592
推荐引用方式 GB/T 7714	Jayanthi, Sri Lakshmi Sesha Vani,Keesara, Venkata Reddy,Sridhar, Venkataramana. Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)[J],2022,14(12).
APA	Jayanthi, Sri Lakshmi Sesha Vani,Keesara, Venkata Reddy,&Sridhar, Venkataramana.(2022).Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR).SUSTAINABILITY,14(12).
MLA	Jayanthi, Sri Lakshmi Sesha Vani,et al."Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)".SUSTAINABILITY 14.12(2022).