气候变化领域集成服务门户(CCPortal): LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios

CCPortal

DOI	10.1007/s00477-021-01969-3
	LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios
	Ahmed, A. A. Masrur; Deo, Ravinesh C.; Ghahramani, Afshin; Raj, Nawin; Feng, Qi; Yin, Zhenliang; Yang, Linshan
发表日期	2021
ISSN	1436-3240
EISSN	1436-3259
起始页码	1851
结束页码	1881
卷号	35 期号:9
英文摘要	Future soil moisture (SM) estimation is a practically useful task for eco-hydrologists, agriculturists, and stakeholders in environment health monitoring to generate comprehensive understanding of hydro-physical and soil dynamic system. This paper demonstrates the capability of a hybridised long short-term memory (LSTM) predictive framework to emulate SM under global warming scenarios. The proposed model is developed by integrating Boruta-random forest (BRF) feature selection and capturing significant antecedent memory of SM behaviour were applied to estimate the future SM using Coupled Model Intercomparison Phase-5 (CMIP5) repository. The BRF is adapted to extract pertinent features in hydro-meteorological variables intrinsically related to SM, and therefore, is used to construct a hybridised deep learning (i.e., BRF-LSTM) model. To establish the viability of deep learning model for SM estimation until 2100, five stations closely matched to the global climate model grid are selected in Australia's Murray Darling Basin. The performance skill of BRF-LSTM model is compared against standalone models (i.e., LSTM, SVR, and MARS). The results showed that the hybrid deep learning model (i.e., BRF-LSTM) with a feature selection capability could significantly outperform the standalone models for both warming simulations. The proposed hybrid model also demonstrated superiority in SM estimation with over 95% of all predictive errors lying below 0.02 mm, and low relative root means square error (approximate to 1.06% for RCP4.5 and approximate to 1.888% for RCP8.5) to outperform all the benchmark models. This study demonstrates the capability of LSTM algorithm coupled with BRF feature selection to simulate future soil moisture under climate change, and so, can be successfully implemented in hydrology, agriculture, soil use management and environmental management.
英文关键词	Soil moisture; Hybrid model; Boruta-random forest optimiser algorithm (BRF); Global climate model (GCM); Murray darling basin; Long short-term memory (LSTM)
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources
WOS类目	Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources
WOS关键词	EMPIRICAL MODE DECOMPOSITION ; SUPPORT VECTOR MACHINE ; ABSOLUTE ERROR MAE ; CLIMATE MODELS ; REFINED INDEX ; WATER-LEVEL ; WIND-SPEED ; CMIP5 ; RAINFALL ; DROUGHT
WOS记录号	WOS:000607339900001
来源期刊	STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/239447
作者单位	[Ahmed, A. A. Masrur; Deo, Ravinesh C.; Raj, Nawin] Univ Southern Queensland, Sch Sci, Springfield, Qld 4300, Australia; [Ghahramani, Afshin] Univ Southern Queensland, Ctr Sustainable Agr Syst, Springfield, Qld 4300, Australia; [Feng, Qi; Yin, Zhenliang; Yang, Linshan] Chinese Acad Sci, Key Lab Ecohydrol Inland River Basin, Beijing, Peoples R China; [Feng, Qi; Yin, Zhenliang; Yang, Linshan] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Donggang West Rd 320, Lanzhou 730000, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Ahmed, A. A. Masrur,Deo, Ravinesh C.,Ghahramani, Afshin,et al. LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios[J]. 中国科学院西北生态环境资源研究院,2021,35(9).
APA	Ahmed, A. A. Masrur.,Deo, Ravinesh C..,Ghahramani, Afshin.,Raj, Nawin.,Feng, Qi.,...&Yang, Linshan.(2021).LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,35(9).
MLA	Ahmed, A. A. Masrur,et al."LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 35.9(2021).