气候变化领域集成服务门户(CCPortal): Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity

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DOI	10.1016/j.jhydrol.2021.126350
	Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity
	Ahmed, A. A. Masrur; Deo, Ravinesh C.; Feng, Qi; Ghahramani, Afshin; Raj, Nawin; Yin, Zhenliang; Yang, Linshan
发表日期	2021
ISSN	0022-1694
EISSN	1879-2707
卷号	599
英文摘要	Long-term forecasting of any hydrologic phenomena is essential for strategic environmental planning, hydrologic and other forms of structural design, agriculture, and water resources management. Climate mode indices, utilising machine learning methods, are frequently considered as predictor variables in order to forecast several different hydrological variables. In this study, a feature selection algorithm based on two different deep learning models, i.e., long short-term memory and a gated recurrent unit, is applied to improve the forecasting capability of streamflow water levels at six gauging stations in the Murray Darling Basin of Australia. This paper therefore aggregates the significant antecedent lag memory of climate mode indices, rainfall, and the monthly factor based on the periodicity as the predictor variables to attain significantly accurate stream water level forecasts. This novel method identifies an improved relationship between the stream water level and climate mode indices through the aggregation of the significant lagged datasets capturing the historical features to predict the future streamflow water level. The boruta feature selection algorithm (BRF) was then applied in a two phase process before and after attaining the significant lagged inputs to screen the optimum predictor variables. The merits of the forecast models were evaluated through different performance evaluation criteria. The results show that the accumulated significant lagged inputs based on climate mode indices, along with the rainfall and periodicity factors are seen to provide improved forecasting of the SWL over the non-BRF deep learning approaches where no prior feature selection was applied. The hybrid LSTM method (i.e., BRF-LSTM model) achieved a unique advantage in terms of SWL forecasting, particularly attaining over 98% of the predictive errors lying within a band of +/-0.015 m with relatively low relative errors (RRMSE approximate to 1.30% and RMAE approximate to 0.882%), outperforming all of the benchmark models. It is also found that the periodicity factor has a potential influence on the accuracy of the forecast models for the four monitored study stations. This study concludes that the newly developed hybrid deep learning approaches, coupled with the BRF feature selection, provide improved forecasting performance. The hybrid approach developed in this paper can therefore be used to provide a strong provide predictive response algorithm for the hydrological variables that were influenced by the low-frequency variability of the climate model indices in respect to streamflow water level.
英文关键词	Stream water level; Climate indices; Boruta-random forest hybridizer algorithm (BRF); Significant lag memory; Murray Darling Basin; long short-term memory (LSTM)
WOS研究方向	Engineering ; Geology ; Water Resources
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS关键词	SUPPORT VECTOR MACHINE ; NINO SOUTHERN-OSCILLATION ; ABSOLUTE ERROR MAE ; AUSTRALIAN RAINFALL ; SEASONAL RAINFALL ; NEURAL-NETWORK ; WATER-RESOURCES ; REFINED INDEX ; DROUGHT ; VARIABILITY
WOS记录号	WOS:000673486000023
来源期刊	JOURNAL OF HYDROLOGY
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/239435
作者单位	[Ahmed, A. A. Masrur; Deo, Ravinesh C.; Raj, Nawin] Univ Southern Queensland, Sch Sci, 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; [Ghahramani, Afshin] Univ Southern Queensland, Ctr Sustainable Agr Syst, Toowoomba, Qld 4500, Australia
推荐引用方式 GB/T 7714	Ahmed, A. A. Masrur,Deo, Ravinesh C.,Feng, Qi,et al. Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity[J]. 中国科学院西北生态环境资源研究院,2021,599.
APA	Ahmed, A. A. Masrur.,Deo, Ravinesh C..,Feng, Qi.,Ghahramani, Afshin.,Raj, Nawin.,...&Yang, Linshan.(2021).Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity.JOURNAL OF HYDROLOGY,599.
MLA	Ahmed, A. A. Masrur,et al."Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity".JOURNAL OF HYDROLOGY 599(2021).