干旱区生态环境知识资源中心(Arid): A novel approach to forecast water table rise in arid regions using stacked ensemble machine learning and deep artificial intelligence models

Arid

DOI	10.1016/j.jhydrol.2024.131668
	A novel approach to forecast water table rise in arid regions using stacked ensemble machine learning and deep artificial intelligence models
	Elzain, Hussam Eldin; Abdalla, Osman; Al-Maktoumi, Ali; Kacimov, Anvar; Eltayeb, Mohamed
通讯作者	Abdalla, O
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2024
卷号	640
英文摘要	Accurate prediction of water table rise (WTR), or groundwater flooding is crucial for water resources management, flood risk mitigation, infrastructure planning, and environmental protection. This study utilized novel shallow learning (SL) and deep learning (DL) models for WTR forecasting for one-, two-, and three-week steps ahead at two levels of modeling in Muscat Governate, Oman. At Level 1, the predictive outcomes of SL models (CBR, XGB, LGBM) and DL models (LSTM, GRU, TR) were utilized as input for producing stacked SL and DL models at level 2 to improve the overall forecasting. This research used a total of 19,465 datasets with highresolution (half-hour interval) measurements between Dec 2017 and Jan 2019. The data were split into training and testing sets, with 90% for training (the first 17,976 datasets) and 10% (the remaining 1,489 datasets) for testing. Feature engineering techniques, such as lags, differences, and aggregates of WTR, were used as input data for the individual SL and DL models at level 1 of modeling. The novel rolling forecasting method was applied alongside the models to capture patterns and trends changes over time. The results showed that the stacked SL and DL models at the level 2 strategy outperformed the standalone SL and DL models at level 2, based on NSE and RSR statistical metrics of the testing data. Furthermore, the modeling of one-week step-ahead provided more accurate WTR forecasting compared with the two- and three-week step-ahead forecasts. The improvement percentage of the stacked SL and DL models reached 32.80%, and there was a 41.17% reduction in RSR, respectively. The approach of this research could be effectively applied to several arid regions affected by WTR worldwide.
英文关键词	Water-table rise Shallow learning Deep learning Stacking model Modeling at two levels of strategy
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001280841000001
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404602
推荐引用方式 GB/T 7714	Elzain, Hussam Eldin,Abdalla, Osman,Al-Maktoumi, Ali,et al. A novel approach to forecast water table rise in arid regions using stacked ensemble machine learning and deep artificial intelligence models[J],2024,640.
APA	Elzain, Hussam Eldin,Abdalla, Osman,Al-Maktoumi, Ali,Kacimov, Anvar,&Eltayeb, Mohamed.(2024).A novel approach to forecast water table rise in arid regions using stacked ensemble machine learning and deep artificial intelligence models.JOURNAL OF HYDROLOGY,640.
MLA	Elzain, Hussam Eldin,et al."A novel approach to forecast water table rise in arid regions using stacked ensemble machine learning and deep artificial intelligence models".JOURNAL OF HYDROLOGY 640(2024).