干旱区生态环境知识资源中心(Arid): Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

Arid

DOI	10.3390/su15053874
	Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco
	Ouali, Lamya; Kabiri, Lahcen; Namous, Mustapha; Hssaisoune, Mohammed; Abdelrahman, Kamal; Fnais, Mohammed S. S.; Kabiri, Hichame; El Hafyani, Mohammed; Oubaassine, Hassane; Arioua, Abdelkrim; Bouchaou, Lhoussaine
通讯作者	Ouali, L
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2023
卷号	15 期号:5
英文摘要	Water availability is a key factor in territorial sustainable development. Moreover, groundwater constitutes the survival element of human life and ecosystems in arid oasis areas. Therefore, groundwater potential (GWP) identification represents a crucial step for its management and sustainable development. This study aimed to map the GWP using ten algorithms, i.e., shallow models comprising: multilayer perceptron, k-nearest neighbor, decision tree, and support vector machine algorithms; hybrid models comprising: voting, random forest, adaptive boosting, gradient boosting (GraB), and extreme gradient boosting; and the deep learning neural network. The GWP inventory map was prepared using 884 binary data, with 1 indicating a high GWP and 0 indicating an extremely low GWP. Twenty-three GWP-influencing factors have been classified into numerical data using the frequency ration method. Afterwards, they were selected based on their importance and multi-collinearity tests. The predicted GWP maps show that, on average, only 11% of the total area was predicted as a very high GWP zone and 17% and 51% were estimated as low and very low GWP zones, respectively. The performance analyses demonstrate that the applied algorithms have satisfied the validation standards for both training and validation tests with an average area under curve of 0.89 for the receiver operating characteristic. Furthermore, the models' prioritization has selected the GraB model as the outperforming algorithm for GWP mapping. This study provides decision support tools for sustainable development in an oasis area.
英文关键词	groundwater potential spatial prediction machine learning performance water supply oasis
类型	Article
语种	英语
开放获取类型	Green Submitted, gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000947625000001
WOS关键词	RESOURCES ; MODEL
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398771
推荐引用方式 GB/T 7714	Ouali, Lamya,Kabiri, Lahcen,Namous, Mustapha,et al. Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco[J],2023,15(5).
APA	Ouali, Lamya.,Kabiri, Lahcen.,Namous, Mustapha.,Hssaisoune, Mohammed.,Abdelrahman, Kamal.,...&Bouchaou, Lhoussaine.(2023).Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco.SUSTAINABILITY,15(5).
MLA	Ouali, Lamya,et al."Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco".SUSTAINABILITY 15.5(2023).