气候变化领域集成服务门户(CCPortal): Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas

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DOI	10.3390/w13182558
	Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas
	Liu, Wei; Yu, Haijiao; Yang, Linshan; Yin, Zhenliang; Zhu, Meng; Wen, Xiaohu
通讯作者	Wen, XH (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Ecohydrol Inland River Basin, Lanzhou 730000, Peoples R China. ; Yu, HJ (通讯作者)，Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China.
发表日期	2021
EISSN	2073-4441
卷号	13 期号:18
英文摘要	An accurate groundwater level (GWL) forecast at multi timescales is vital for agricultural management and water resource scheduling in arid irrigated areas such as the Hexi Corridor, China. However, the forecast of GWL in these areas remains a challenging task owing to the deficient hydrogeological data and the highly nonlinear, non-stationary and complex groundwater system. The development of reliable groundwater level simulation models is necessary and profound. In this study, a novel ensemble deep learning GWL predictive framework integrating data pro-processing, feature selection, deep learning and uncertainty analysis was constructed. Under this framework, a hybrid model equipped with currently the most effective algorithms, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for data decomposition, the genetic algorithm (GA) for feature selection, the deep belief network (DBN) model, and the quantile regression (QR) for uncertainty evaluation, denoted as CEEMDAN-GA-DBN, was proposed for the 1-, 2-, and 3-month ahead GWL forecast at three GWL observation wells in the Jiuquan basin, northwest China. The capability of the CEEMDAN-GA-DBN model was compared with the hybrid CEEMDAN-DBN and the standalone DBN model in terms of the performance metrics including R, MAE, RMSE, NSE, RSR, AIC and the Legates and McCabe's Index as well as the uncertainty criterion including MPI and PICP. The results demonstrated the higher degree of accuracy and better performance of the objective CEEMDAN-GA-DBN model than the CEEMDAN-DBN and DBN models at all lead times and all the wells. Overall, the CEEMDAN-GA-DBN reduced the RMSE of the CEEMDAN-DBN and DBN models in the testing period by about 9.16 and 17.63%, while it improved their NSE by about 6.38 and 15.32%, respectively. The uncertainty analysis results also affirmed the slightly better reliability of the CEEMDAN-GA-DBN method than the CEEMDAN-DBN and DBN models at the 1-, 2- and 3-month forecast horizons. The derived results proved the ability of the proposed ensemble deep learning model in multi time steps ahead of GWL forecasting, and thus, can be used as an effective tool for GWL forecasting in arid irrigated areas.
关键词	EMPIRICAL MODE DECOMPOSITION WAVELET ANALYSIS QUANTILE REGRESSION ALGORITHM DEPTH QUANTIFICATION UNCERTAINTY RECHARGE MACHINES DESIGN
英文关键词	groundwater level forecasting; complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); genetic algorithm (GA); feature selection (FS); deep belief network (DBN); quantile regression (QR); uncertainty evaluation
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
WOS类目	Environmental Sciences ; Water Resources
WOS记录号	WOS:000701552600001
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/253995
作者单位	[Liu, Wei; Yang, Linshan; Yin, Zhenliang; Zhu, Meng; Wen, Xiaohu] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Ecohydrol Inland River Basin, Lanzhou 730000, Peoples R China; [Yu, Haijiao] Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China
推荐引用方式 GB/T 7714	Liu, Wei,Yu, Haijiao,Yang, Linshan,et al. Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas[J]. 中国科学院西北生态环境资源研究院,2021,13(18).
APA	Liu, Wei,Yu, Haijiao,Yang, Linshan,Yin, Zhenliang,Zhu, Meng,&Wen, Xiaohu.(2021).Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas.WATER,13(18).
MLA	Liu, Wei,et al."Deep Learning-Based Predictive Framework for Groundwater Level Forecast in Arid Irrigated Areas".WATER 13.18(2021).