气候变化领域集成服务门户(CCPortal): A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods

CCPortal

DOI	10.3390/su14052601
	A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods
	Kadkhodazadeh, Mojtaba; Valikhan Anaraki, Mahdi; Morshed-Bozorgdel, Amirreza; Farzin, Saeed
发表日期	2022
EISSN	2071-1050
卷号	14 期号:5
英文摘要	In the present study, a new methodology for reference evapotranspiration (ETo) prediction and uncertainty analysis under climate change and COVID-19 post-pandemic recovery scenarios for the period 2021-2050 at nine stations in the two basins of Lake Urmia and Sefidrood is presented. For this purpose, firstly ETo data were estimated using meteorological data and the FAO Penman-Monteith (FAO-56 PM) method. Then, ETo modeling by six machine learning techniques including multiple linear regression (MLR), multiple non-linear regression (MNLR), multivariate adaptive regression splines (MARS), model tree M5 (M5), random forest (RF) and least-squares boost (LSBoost) was carried out. The technique for order of preference by similarity to ideal solution (TOPSIS) method was used under seven scenarios to rank models with evaluation and time criteria in the next step. After proving the acceptable performance of the LSBoost model, the downscaling of temperature (T) and precipitation (P) by the delta change factor (CF) method under three models ACCESS-ESM1-5, CanESM5 and MRI-ESM2-0 (scenarios SSP245-cov-fossil (SCF), SSP245-cov-modgreen (SCM) and SSP245-cov-strgreen (SCS)) was performed. The results showed that the monthly changes in the average T increases at all stations for all scenarios. Also, the average monthly change ratio of P increases in most stations and scenarios. In the next step, ETo forecasting under climate change for periods (2021-2050) was performed using the best model. Prediction results showed that ETo increases in all scenarios and stations in a pessimistic and optimistic state. In addition, the Monte Carlo method (MCM) showed that the lowest uncertainty is related to the Mianeh station in the MRI-ESM2-0 model and SCS scenario.
英文关键词	reference evapotranspiration; machine learning; TOPSIS; Monte Carlo method; climate change; uncertainty analysis; Lake Urmia; Sefidrood
语种	英语
WOS研究方向	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS类目	Science Citation Index Expanded (SCI-EXPANDED) ; Social Science Citation Index (SSCI)
WOS记录号	WOS:000768478700001
来源期刊	SUSTAINABILITY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/280476
作者单位	Semnan University
推荐引用方式 GB/T 7714	Kadkhodazadeh, Mojtaba,Valikhan Anaraki, Mahdi,Morshed-Bozorgdel, Amirreza,et al. A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods[J],2022,14(5).
APA	Kadkhodazadeh, Mojtaba,Valikhan Anaraki, Mahdi,Morshed-Bozorgdel, Amirreza,&Farzin, Saeed.(2022).A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods.SUSTAINABILITY,14(5).
MLA	Kadkhodazadeh, Mojtaba,et al."A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods".SUSTAINABILITY 14.5(2022).