干旱区生态环境知识资源中心(Arid): Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting

Arid

DOI	10.1016/j.engappai.2024.108744
	Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting
	Lee, Jinwook; Bateni, Sayed M.; Jun, Changhyun; Heggy, Essam; Jamei, Mehdi; Kim, Dongkyun; Ghafouri, Hamid Reza; Deenik, Jonathan L.
通讯作者	Jun, C
来源期刊	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
ISSN	0952-1976
EISSN	1873-6769
出版年	2024
卷号	135
英文摘要	Evapotranspiration is an essential component of the hydrological cycle. Forecasting the reference crop evapotranspiration (ET o ) using a reliable and generalized framework is crucial for agricultural operations, especially irrigation. This study was aimed at evaluating the performance of a hybrid system including the K -Best selection (KBest), multivariate variational mode decomposition (MVMD), and Machine learning (ML) models for 1-, 3-, 7-, and 10 -day -ahead forecasting of the daily ET o in twelve stations of California. The analysis covered a span of 20 years, from 2003 to 2022. Three stand-alone ML models, namely Cascade Forward Neural Network (CFNN), Extreme Learning Machine (ELM), and Bagging Regression Tree (BRT) are used and were integrated with various preprocessing techniques to construct three hybrid models, i.e., MVMD-KBest-CFNN, MVMD-KBest-ELM, and MVMD-KBest-BRT. According to the results obtained in the testing phase, averaged across all stations, all three stand-alone models (CFNN, ELM, and BRT) yielded similar outcomes. In contrast, the hybrid models exhibited significantly enhanced performances compared with the standalone models, and MVMD-KBest-CFNN and MVMD-KBest-ELM models outperformed MVMD-KBest-BRT model. The BRT-based models were vulnerable to overfitting. The performance of the best models is superior compared to similar existing studies. Examining the variations across stations, it was found that the stations located further from the coast and in arid regions could be susceptible to prediction errors and necessitate more attention.
英文关键词	Reference evapotranspiration Multitemporal forecasting Multivariate variational mode decomposition KBest selection Hybrid system
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001255936200001
WOS关键词	VARIATIONAL MODE DECOMPOSITION ; SUPPORT VECTOR MACHINE ; NEURAL-NETWORK ; PREDICTION ; REGRESSION ; ALGORITHM ; HYDROLOGY
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403500
推荐引用方式 GB/T 7714	Lee, Jinwook,Bateni, Sayed M.,Jun, Changhyun,et al. Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting[J],2024,135.
APA	Lee, Jinwook.,Bateni, Sayed M..,Jun, Changhyun.,Heggy, Essam.,Jamei, Mehdi.,...&Deenik, Jonathan L..(2024).Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,135.
MLA	Lee, Jinwook,et al."Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 135(2024).