气候变化领域集成服务门户(CCPortal): Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

CCPortal

DOI	10.3390/w13020139
	Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level
	Wu, Min; Feng, Qi; Wen, Xiaohu; Yin, Zhenliang; Yang, Linshan; Sheng, Danrui
发表日期	2021
EISSN	2073-4441
卷号	13 期号:2
英文摘要	Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models considering the uncontrollable error, indefinite inputs and unneglectable uncertainty during the modelling process. The AI model ensembled with the data pretreatment technique, the input selection method, or uncertainty analysis has been successfully used to tackle this issue, whereas studies about the comprehensive deterministic and uncertainty analysis of hybrid models in groundwater level forecast are rarely reported. In this study, a novel hybrid predictive model combining the variational mode decomposition (VMD) data pretreatment technique, Boruta input selection method, bootstrap based uncertainty analysis, and the extreme learning machine (ELM) model named VBELM was developed for 1-, 2- and 3-month ahead groundwater level prediction in a typical arid oasis area of northwestern China. The historical observed monthly groundwater level, precipitation and temperature data were used as inputs to train and test the model. Specifically, the VMD was used to decompose all the input-outputs into a set of intrinsic mode functions (IMFs), the Boruta method was applied to determine input variables, and the ELM was employed to forecast the value of each IMF. In order to ascertain the efficiency of the proposed VBELM model, the performance of the coupled model (VELM) hybridizing VMD with ELM algorithm and the single ELM model were estimated in comparison. The results indicate that the VBELM performed best, while the single ELM model performed the worst among the three models. Furthermore, the VBELM model presented lower uncertainty than the VELM model with more observed groundwater level values falling inside the confidence interval. In summary, the VBELM model demonstrated an excellent performance for both certainty and uncertainty analyses, and can serve as an effective tool for multi-scale groundwater level forecasting.
英文关键词	uncertainty analysis; groundwater level prediction; hybrid predictive model; variational mode decomposition; Boruta technique; extreme learning machine
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
WOS类目	Environmental Sciences ; Water Resources
WOS记录号	WOS:000611775500001
来源期刊	WATER
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/239448
作者单位	[Wu, Min; Feng, Qi; Wen, Xiaohu; Yin, Zhenliang; Yang, Linshan; Sheng, Danrui] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Ecohydrol Inland River Basin, Lanzhou 730000, Peoples R China; [Wu, Min; Sheng, Danrui] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Wu, Min; Feng, Qi; Wen, Xiaohu; Yin, Zhenliang; Yang, Linshan; Sheng, Danrui] Qilian Mt Ecoenvironm Res Ctr Gansu Prov, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Wu, Min,Feng, Qi,Wen, Xiaohu,et al. Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level[J]. 中国科学院西北生态环境资源研究院,2021,13(2).
APA	Wu, Min,Feng, Qi,Wen, Xiaohu,Yin, Zhenliang,Yang, Linshan,&Sheng, Danrui.(2021).Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level.WATER,13(2).
MLA	Wu, Min,et al."Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level".WATER 13.2(2021).