气候变化领域集成服务门户(CCPortal): Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam

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DOI	10.1007/s12145-023-01209-y
	Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam
	Nguyen, Huu Duy; Nguyen, Van Hong; Du, Quan Vu Viet; Nguyen, Cong Tuan; Dang, Dinh Kha; Truong, Quang Hai; Dang, Ngo Bao Toan; Tran, Quang Tuan; Nguyen, Quoc-Huy; Bui, Quang-Thanh
发表日期	2024
ISSN	1865-0473
EISSN	1865-0481
起始页码	17
结束页码	2
卷号	17 期号:2
英文摘要	Groundwater resources are required for domestic water supply, agriculture, and industry, and the strategic importance of water resources will only increase in the context of climate change and population growth. For optimal management of this crucial resource, exploration of the potential of groundwater is necessary. To this end, the objective of this study was the development of a new method based on remote sensing, deep neural networks (DNNs), and the optimization algorithms Adam, Flower Pollination Algorithm (FPA), Artificial Ecosystem-based Optimization (AEO), Pathfinder Algorithm (PFA), African Vultures Optimization Algorithm (AVOA), and Whale Optimization Algorithm (WOA) to predict groundwater potential in the North Central region of Vietnam. 95 springs or wells with 13 conditioning factors were used as input data to the machine learning model to find the statistical relationships between the presence and nonpresence of groundwater and the conditioning factors. Statistical indices, namely root mean square error (RMSE), area under curve (AUC), accuracy, kappa (K) and coefficient of determination (R2), were used to validate the models. The results indicated that all the proposed models were effective in predicting groundwater potential, with AUC values of more than 0.95. Among the proposed models, the DNN-AVOA model was more effective than the other models, with an AUC value of 0.97 and an RMSE of 0.22. This was followed by DNN-PFA (AUC=0.97, RMSE=0.22), DNN-FPA (AUC=0.97, RMSE=0.24), DNN-AEO (AUC=0.96, RMSE=0.25), DNN-Adam (AUC=0.97, RMSE=0.28), and DNN-WOA (AUC=0.95, RMSE=0.3). In addition, according to the groundwater potential map, about 25-30% of the region was in the high and very high potential groundwater zone; 5-10% was in the moderate zone, and 60-70% was low or very low. The results of this study can be used in the management of water resources in general and the location of appropriate wells in particular.
英文关键词	Groundwater; DNN; Water ressources; Machine learning; Vietnam
语种	英语
WOS研究方向	Computer Science ; Geology
WOS类目	Computer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary
WOS记录号	WOS:001141933600001
来源期刊	EARTH SCIENCE INFORMATICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/291046
作者单位	Vietnam National University Hanoi; Vietnam Academy of Science & Technology (VAST); Vietnam National University Hanoi; Quy Nhon University
推荐引用方式 GB/T 7714	Nguyen, Huu Duy,Nguyen, Van Hong,Du, Quan Vu Viet,et al. Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam[J],2024,17(2).
APA	Nguyen, Huu Duy.,Nguyen, Van Hong.,Du, Quan Vu Viet.,Nguyen, Cong Tuan.,Dang, Dinh Kha.,...&Bui, Quang-Thanh.(2024).Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam.EARTH SCIENCE INFORMATICS,17(2).
MLA	Nguyen, Huu Duy,et al."Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam".EARTH SCIENCE INFORMATICS 17.2(2024).