干旱区生态环境知识资源中心(Arid): Estimation of actual evapotranspiration: A novel hybrid method based on remote sensing and artificial intelligence

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DOI	10.1016/j.jhydrol.2022.127774
	Estimation of actual evapotranspiration: A novel hybrid method based on remote sensing and artificial intelligence
	Hadadi, Fatemeh; Moazenzadeh, Roozbeh; Mohammadi, Babak
通讯作者	Moazenzadeh, R
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2022
卷号	609
英文摘要	Actual evapotranspiration (AET) is one of the decisive factors controlling the water balance at the catchment level, particularly in arid and semi-arid regions, but measured data for which are generally unavailable. In this study, performance of a base artificial intelligence (AI) model, adaptive neuro-fuzzy inference system (ANFIS), and its hybrids with two bio-inspired optimization algorithms, namely shuffled frog leaping algorithm (SFLA) and grey wolf optimization (GWO), in estimating monthly AET was evaluated over 2001-2010 across Neishaboor watershed in Iran. The inputs of these models were categorized into three groups including meteorological, remotely sensed, and hybrid-based predictors, and defined in the form of 8 different scenarios. Net radiation (Rn), land surface temperature (LST), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and soil wetness deficit index (SWDI) were the remotely sensed predictors, computed using MODIS satellite images on the monthly scale for the study area. The results showed that the SWDI predictor has played a significant role in improving the accuracy of AET estimation, with the highest error reduction (12.5, 17 and 26.5% for ANFIS, ANFIS-SFLA, and ANFIS-GWO, respectively) obtained under scenarios including SWDI compared to corresponding scenarios excluding this predictor. In testing set, the three aforementioned models exhibited their best performance under Scenario 8 (RMSE = 11.93, NSE = 0.69, RRMSE = 0.37), Scenario 4 (RMSE = 11.06, NSE = 0.74, RRMSE = 0.37) and Scenario 4 (RMSE = 10.9, NSE = 0.76, RRMSE = 0.36), respectively. Coupling the SFLA and GWO optimization algorithms to the base model improved the accuracy of AET estimation, with the maximum error reduction for the two algorithms being about 12% (Scenarios 2 and 4) and 14% (Scenario 4), respectively. Examining the performance of the best scenarios of the three models in three intervals including the first, middle, and last third of measured AET values showed that all models were the most accurate in the first third interval. The results also indicated that all models have had higher accuracies in the first and middle third intervals of under-estimation set and the last interval of over-estimation set.
英文关键词	AET Bio-inspired optimization algorithms Iran Meteorological parameters Remotely sensed predictors
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000790556700003
WOS关键词	FROG-LEAPING ALGORITHM ; NEURAL-NETWORKS ; OPTIMIZATION ; MACHINE ; TEMPERATURE
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393486
推荐引用方式 GB/T 7714	Hadadi, Fatemeh,Moazenzadeh, Roozbeh,Mohammadi, Babak. Estimation of actual evapotranspiration: A novel hybrid method based on remote sensing and artificial intelligence[J],2022,609.
APA	Hadadi, Fatemeh,Moazenzadeh, Roozbeh,&Mohammadi, Babak.(2022).Estimation of actual evapotranspiration: A novel hybrid method based on remote sensing and artificial intelligence.JOURNAL OF HYDROLOGY,609.
MLA	Hadadi, Fatemeh,et al."Estimation of actual evapotranspiration: A novel hybrid method based on remote sensing and artificial intelligence".JOURNAL OF HYDROLOGY 609(2022).