干旱区生态环境知识资源中心(Arid): A high dimensional features-based cascaded forward neural network coupled with MVMD and Boruta-GBDT for multi-step ahead forecasting of surface soil moisture

Arid

DOI	10.1016/j.engappai.2023.105895
	A high dimensional features-based cascaded forward neural network coupled with MVMD and Boruta-GBDT for multi-step ahead forecasting of surface soil moisture
	Jamei, Mehdi; Ali, Mumtaz; Karbasi, Masoud; Sharma, Ekta; Jamei, Mozhdeh; Chu, Xuefeng; Yaseen, Zaher Mundher
通讯作者	Jamei, M
来源期刊	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
ISSN	0952-1976
EISSN	1873-6769
出版年	2023
卷号	120
英文摘要	The objective of this study is to develop a novel multi-level pre-processing framework and apply it for multi-step (one and seven days ahead) daily forecasting of Surface soil moisture (SSM) based on the NASA's Soil Moisture Active Passive (SMAP)-satellite datasets in arid and semi-arid regions of Iran. The framework consists of the Boruta gradient boosting decision tree (Boruta-GBDT) feature selection integrated with the multivariate variational mode decomposition (MVMD) and advanced machine learning (ML) models including bidirectional gated recurrent unit (Bi-GRU), cascaded forward neural network (CFNN), adaptive boosting (AdaBoost), genetic programming (GP), and classical multilayer perceptron neural network (MLP). For this purpose, effective geophysical soil moisture predictors for two arid stations of Khosrowshah and Neyshabur were first filtered among 21 daily input signals from 2015 to 2020 by using the Boruta-GBDT feature selection. The selected signals were then decomposed using the MVMD scheme. In the last pre-processing stage, the most relevant sub-sequences from a large pool in previous process were filtered using the Boruta-GBDT scheme aiming to reduce the computation and enhance the accuracy, before feeding the ML approaches. The comparison of the results from the five hybrid and standalone counterpart models in term of standardized RMSE improvement (SRMSEI) revealed that M-VMD-B-o-C-PNN for SSM(T+1)\| 27.13% and SSM (T+7)\| 43.55% at Khosrowshah station and SSM(T+1)\| 21.16% and SSM (T+7)\| 30.10% at Neyshabur station outperformed the other hybrid frameworks, followed by M-VMD-B-o-B;-Ru, M-VMD-B-G-A(d.hooge), M-VMD-B-o-G(P), and M-VMD-B-o-M-LP. The accurately forecasted SSM data help improve irrigation scheduling, which is of significant importance in water use efficiency and food security.
英文关键词	Surface soil moisture forecasting Microwave remote sensing SMAP Cascaded forward neural network Bidirectional gated recurrent unit Boruta-GBDT Multivariate variational model decomposition
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000963612700001
WOS关键词	MODEL ; DYNAMICS ; IMAGERY ; SYSTEM
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/2XILL650/396079
推荐引用方式 GB/T 7714	Jamei, Mehdi,Ali, Mumtaz,Karbasi, Masoud,et al. A high dimensional features-based cascaded forward neural network coupled with MVMD and Boruta-GBDT for multi-step ahead forecasting of surface soil moisture[J],2023,120.
APA	Jamei, Mehdi.,Ali, Mumtaz.,Karbasi, Masoud.,Sharma, Ekta.,Jamei, Mozhdeh.,...&Yaseen, Zaher Mundher.(2023).A high dimensional features-based cascaded forward neural network coupled with MVMD and Boruta-GBDT for multi-step ahead forecasting of surface soil moisture.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,120.
MLA	Jamei, Mehdi,et al."A high dimensional features-based cascaded forward neural network coupled with MVMD and Boruta-GBDT for multi-step ahead forecasting of surface soil moisture".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 120(2023).